Overview

Dataset statistics

Number of variables31
Number of observations28372
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.7 MiB
Average record size in memory248.0 B

Variable types

Numeric26
Text3
Categorical2

Alerts

Unnamed: 0 is highly overall correlated with genreHigh correlation
acousticness is highly overall correlated with energy and 1 other fieldsHigh correlation
age is highly overall correlated with loudness and 1 other fieldsHigh correlation
energy is highly overall correlated with acousticness and 1 other fieldsHigh correlation
genre is highly overall correlated with Unnamed: 0High correlation
loudness is highly overall correlated with acousticness and 3 other fieldsHigh correlation
release_date is highly overall correlated with age and 1 other fieldsHigh correlation
topic is highly overall correlated with world/lifeHigh correlation
world/life is highly overall correlated with topicHigh correlation
Unnamed: 0 has unique valuesUnique
lyrics has unique valuesUnique
instrumentalness has 7870 (27.7%) zerosZeros

Reproduction

Analysis started2024-01-19 01:02:58.827141
Analysis finished2024-01-19 01:04:30.171342
Duration1 minute and 31.34 seconds
Software versionydata-profiling vv4.6.4
Download configurationconfig.json

Variables

Unnamed: 0
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct28372
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean42946.324
Minimum0
Maximum82451
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size221.8 KiB
2024-01-18T20:04:30.287698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3988.95
Q120391.25
median45405.5
Q364090.5
95-th percentile79456.45
Maximum82451
Range82451
Interquartile range (IQR)43699.25

Descriptive statistics

Standard deviation24749.325
Coefficient of variation (CV)0.57628508
Kurtosis-1.2886689
Mean42946.324
Median Absolute Deviation (MAD)21927
Skewness-0.09745296
Sum1.2184731 × 109
Variance6.1252911 × 108
MonotonicityStrictly increasing
2024-01-18T20:04:30.459482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1
 
< 0.1%
58676 1
 
< 0.1%
58697 1
 
< 0.1%
58696 1
 
< 0.1%
58694 1
 
< 0.1%
58691 1
 
< 0.1%
58689 1
 
< 0.1%
58688 1
 
< 0.1%
58686 1
 
< 0.1%
58684 1
 
< 0.1%
Other values (28362) 28362
> 99.9%
ValueCountFrequency (%)
0 1
< 0.1%
4 1
< 0.1%
6 1
< 0.1%
10 1
< 0.1%
12 1
< 0.1%
14 1
< 0.1%
15 1
< 0.1%
17 1
< 0.1%
20 1
< 0.1%
23 1
< 0.1%
ValueCountFrequency (%)
82451 1
< 0.1%
82450 1
< 0.1%
82449 1
< 0.1%
82448 1
< 0.1%
82447 1
< 0.1%
82446 1
< 0.1%
82445 1
< 0.1%
82442 1
< 0.1%
82440 1
< 0.1%
82439 1
< 0.1%
Distinct5426
Distinct (%)19.1%
Missing0
Missing (%)0.0%
Memory size221.8 KiB
2024-01-18T20:04:30.686847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length46
Median length37
Mean length12.100416
Min length1

Characters and Unicode

Total characters343313
Distinct characters91
Distinct categories11 ?
Distinct scripts4 ?
Distinct blocks5 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2118 ?
Unique (%)7.5%

Sample

1st rowmukesh
2nd rowfrankie laine
3rd rowjohnnie ray
4th rowpérez prado
5th rowgiorgos papadopoulos
ValueCountFrequency (%)
the 3698
 
6.3%
864
 
1.5%
band 367
 
0.6%
john 364
 
0.6%
johnny 340
 
0.6%
black 293
 
0.5%
george 289
 
0.5%
king 245
 
0.4%
brothers 236
 
0.4%
of 231
 
0.4%
Other values (6268) 51865
88.2%
2024-01-18T20:04:31.101452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 35133
 
10.2%
30420
 
8.9%
a 27983
 
8.2%
n 22994
 
6.7%
r 21939
 
6.4%
o 20333
 
5.9%
i 19796
 
5.8%
t 19667
 
5.7%
s 19247
 
5.6%
l 18405
 
5.4%
Other values (81) 107396
31.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 309030
90.0%
Space Separator 30421
 
8.9%
Other Punctuation 2580
 
0.8%
Decimal Number 911
 
0.3%
Dash Punctuation 310
 
0.1%
Currency Symbol 22
 
< 0.1%
Math Symbol 19
 
< 0.1%
Other Letter 11
 
< 0.1%
Close Punctuation 5
 
< 0.1%
Final Punctuation 2
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 35133
 
11.4%
a 27983
 
9.1%
n 22994
 
7.4%
r 21939
 
7.1%
o 20333
 
6.6%
i 19796
 
6.4%
t 19667
 
6.4%
s 19247
 
6.2%
l 18405
 
6.0%
h 13548
 
4.4%
Other values (44) 89985
29.1%
Other Punctuation
ValueCountFrequency (%)
. 916
35.5%
& 844
32.7%
' 310
 
12.0%
, 248
 
9.6%
" 121
 
4.7%
! 82
 
3.2%
/ 45
 
1.7%
* 6
 
0.2%
4
 
0.2%
: 3
 
0.1%
Decimal Number
ValueCountFrequency (%)
1 184
20.2%
0 172
18.9%
3 90
9.9%
5 85
9.3%
9 83
9.1%
4 78
8.6%
2 74
8.1%
7 65
 
7.1%
8 64
 
7.0%
6 16
 
1.8%
Other Letter
ValueCountFrequency (%)
º 4
36.4%
2
18.2%
2
18.2%
1
 
9.1%
1
 
9.1%
1
 
9.1%
Space Separator
ValueCountFrequency (%)
30420
> 99.9%
  1
 
< 0.1%
Dash Punctuation
ValueCountFrequency (%)
- 309
99.7%
1
 
0.3%
Math Symbol
ValueCountFrequency (%)
+ 18
94.7%
= 1
 
5.3%
Currency Symbol
ValueCountFrequency (%)
$ 22
100.0%
Close Punctuation
ValueCountFrequency (%)
) 5
100.0%
Final Punctuation
ValueCountFrequency (%)
2
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 309025
90.0%
Common 34272
 
10.0%
Cyrillic 9
 
< 0.1%
Han 7
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 35133
 
11.4%
a 27983
 
9.1%
n 22994
 
7.4%
r 21939
 
7.1%
o 20333
 
6.6%
i 19796
 
6.4%
t 19667
 
6.4%
s 19247
 
6.2%
l 18405
 
6.0%
h 13548
 
4.4%
Other values (36) 89980
29.1%
Common
ValueCountFrequency (%)
30420
88.8%
. 916
 
2.7%
& 844
 
2.5%
' 310
 
0.9%
- 309
 
0.9%
, 248
 
0.7%
1 184
 
0.5%
0 172
 
0.5%
" 121
 
0.4%
3 90
 
0.3%
Other values (21) 658
 
1.9%
Cyrillic
ValueCountFrequency (%)
т 1
11.1%
н 1
11.1%
а 1
11.1%
и 1
11.1%
л 1
11.1%
о 1
11.1%
ф 1
11.1%
э 1
11.1%
д 1
11.1%
Han
ValueCountFrequency (%)
2
28.6%
2
28.6%
1
14.3%
1
14.3%
1
14.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 342998
99.9%
None 292
 
0.1%
Cyrillic 9
 
< 0.1%
Punctuation 7
 
< 0.1%
CJK 7
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 35133
 
10.2%
30420
 
8.9%
a 27983
 
8.2%
n 22994
 
6.7%
r 21939
 
6.4%
o 20333
 
5.9%
i 19796
 
5.8%
t 19667
 
5.7%
s 19247
 
5.6%
l 18405
 
5.4%
Other values (43) 107081
31.2%
None
ValueCountFrequency (%)
é 95
32.5%
ö 94
32.2%
ü 54
18.5%
ÿ 8
 
2.7%
ó 6
 
2.1%
ø 5
 
1.7%
º 4
 
1.4%
í 4
 
1.4%
ú 3
 
1.0%
ë 3
 
1.0%
Other values (11) 16
 
5.5%
Punctuation
ValueCountFrequency (%)
4
57.1%
2
28.6%
1
 
14.3%
CJK
ValueCountFrequency (%)
2
28.6%
2
28.6%
1
14.3%
1
14.3%
1
14.3%
Cyrillic
ValueCountFrequency (%)
т 1
11.1%
н 1
11.1%
а 1
11.1%
и 1
11.1%
л 1
11.1%
о 1
11.1%
ф 1
11.1%
э 1
11.1%
д 1
11.1%
Distinct23689
Distinct (%)83.5%
Missing0
Missing (%)0.0%
Memory size221.8 KiB
2024-01-18T20:04:31.392139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length163
Median length92
Mean length16.224905
Min length1

Characters and Unicode

Total characters460333
Distinct characters152
Distinct categories15 ?
Distinct scripts7 ?
Distinct blocks8 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique21073 ?
Unique (%)74.3%

Sample

1st rowmohabbat bhi jhoothi
2nd rowi believe
3rd rowcry
4th rowpatricia
5th rowapopse eida oneiro
ValueCountFrequency (%)
the 3955
 
4.3%
you 2232
 
2.4%
i 1826
 
2.0%
a 1554
 
1.7%
love 1517
 
1.7%
me 1508
 
1.6%
of 1472
 
1.6%
my 1403
 
1.5%
to 1360
 
1.5%
in 1355
 
1.5%
Other values (11076) 73229
80.1%
2024-01-18T20:04:31.893847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
63039
13.7%
e 45975
 
10.0%
o 34460
 
7.5%
t 29724
 
6.5%
a 29354
 
6.4%
i 27022
 
5.9%
n 26397
 
5.7%
r 22108
 
4.8%
s 21442
 
4.7%
l 20858
 
4.5%
Other values (142) 139954
30.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 385904
83.8%
Space Separator 63039
 
13.7%
Other Punctuation 6827
 
1.5%
Open Punctuation 1526
 
0.3%
Close Punctuation 1526
 
0.3%
Decimal Number 999
 
0.2%
Dash Punctuation 365
 
0.1%
Other Letter 62
 
< 0.1%
Final Punctuation 54
 
< 0.1%
Currency Symbol 15
 
< 0.1%
Other values (5) 16
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 45975
11.9%
o 34460
 
8.9%
t 29724
 
7.7%
a 29354
 
7.6%
i 27022
 
7.0%
n 26397
 
6.8%
r 22108
 
5.7%
s 21442
 
5.6%
l 20858
 
5.4%
h 17598
 
4.6%
Other values (46) 110966
28.8%
Other Letter
ValueCountFrequency (%)
ب 3
 
4.8%
ل 3
 
4.8%
ى 2
 
3.2%
2
 
3.2%
2
 
3.2%
ي 2
 
3.2%
ح 2
 
3.2%
2
 
3.2%
ف 2
 
3.2%
ة 1
 
1.6%
Other values (41) 41
66.1%
Other Punctuation
ValueCountFrequency (%)
' 4242
62.1%
. 964
 
14.1%
, 756
 
11.1%
& 218
 
3.2%
? 213
 
3.1%
/ 143
 
2.1%
! 125
 
1.8%
" 50
 
0.7%
* 46
 
0.7%
: 44
 
0.6%
Other values (5) 26
 
0.4%
Decimal Number
ValueCountFrequency (%)
0 192
19.2%
1 190
19.0%
2 143
14.3%
9 101
10.1%
5 81
8.1%
3 72
 
7.2%
4 72
 
7.2%
6 53
 
5.3%
8 48
 
4.8%
7 47
 
4.7%
Math Symbol
ValueCountFrequency (%)
+ 3
42.9%
= 2
28.6%
> 1
 
14.3%
| 1
 
14.3%
Open Punctuation
ValueCountFrequency (%)
( 1501
98.4%
[ 25
 
1.6%
Close Punctuation
ValueCountFrequency (%)
) 1501
98.4%
] 25
 
1.6%
Dash Punctuation
ValueCountFrequency (%)
- 363
99.5%
2
 
0.5%
Final Punctuation
ValueCountFrequency (%)
53
98.1%
1
 
1.9%
Other Symbol
ValueCountFrequency (%)
° 4
80.0%
® 1
 
20.0%
Initial Punctuation
ValueCountFrequency (%)
1
50.0%
1
50.0%
Space Separator
ValueCountFrequency (%)
63039
100.0%
Currency Symbol
ValueCountFrequency (%)
$ 15
100.0%
Modifier Symbol
ValueCountFrequency (%)
´ 1
100.0%
Control
ValueCountFrequency (%)
“ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 385889
83.8%
Common 74367
 
16.2%
Han 35
 
< 0.1%
Arabic 24
 
< 0.1%
Cyrillic 15
 
< 0.1%
Hangul 2
 
< 0.1%
Hiragana 1
 
< 0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
63039
84.8%
' 4242
 
5.7%
( 1501
 
2.0%
) 1501
 
2.0%
. 964
 
1.3%
, 756
 
1.0%
- 363
 
0.5%
& 218
 
0.3%
? 213
 
0.3%
0 192
 
0.3%
Other values (35) 1378
 
1.9%
Latin
ValueCountFrequency (%)
e 45975
11.9%
o 34460
 
8.9%
t 29724
 
7.7%
a 29354
 
7.6%
i 27022
 
7.0%
n 26397
 
6.8%
r 22108
 
5.7%
s 21442
 
5.6%
l 20858
 
5.4%
h 17598
 
4.6%
Other values (35) 110951
28.8%
Han
ValueCountFrequency (%)
2
 
5.7%
2
 
5.7%
2
 
5.7%
1
 
2.9%
1
 
2.9%
1
 
2.9%
1
 
2.9%
1
 
2.9%
1
 
2.9%
1
 
2.9%
Other values (22) 22
62.9%
Arabic
ValueCountFrequency (%)
ب 3
12.5%
ل 3
12.5%
ى 2
 
8.3%
ي 2
 
8.3%
ح 2
 
8.3%
ف 2
 
8.3%
ة 1
 
4.2%
ج 1
 
4.2%
س 1
 
4.2%
ت 1
 
4.2%
Other values (6) 6
25.0%
Cyrillic
ValueCountFrequency (%)
а 3
20.0%
у 2
13.3%
в 2
13.3%
т 1
 
6.7%
е 1
 
6.7%
л 1
 
6.7%
к 1
 
6.7%
ы 1
 
6.7%
з 1
 
6.7%
м 1
 
6.7%
Hangul
ValueCountFrequency (%)
1
50.0%
1
50.0%
Hiragana
ValueCountFrequency (%)
1
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 460111
> 99.9%
None 86
 
< 0.1%
Punctuation 59
 
< 0.1%
CJK 35
 
< 0.1%
Arabic 24
 
< 0.1%
Cyrillic 15
 
< 0.1%
Hangul 2
 
< 0.1%
Hiragana 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
63039
13.7%
e 45975
 
10.0%
o 34460
 
7.5%
t 29724
 
6.5%
a 29354
 
6.4%
i 27022
 
5.9%
n 26397
 
5.7%
r 22108
 
4.8%
s 21442
 
4.7%
l 20858
 
4.5%
Other values (50) 139732
30.4%
Punctuation
ValueCountFrequency (%)
53
89.8%
2
 
3.4%
1
 
1.7%
1
 
1.7%
1
 
1.7%
1
 
1.7%
None
ValueCountFrequency (%)
é 25
29.1%
á 14
16.3%
ó 6
 
7.0%
í 4
 
4.7%
° 4
 
4.7%
ü 4
 
4.7%
ñ 4
 
4.7%
ú 4
 
4.7%
ç 3
 
3.5%
å 2
 
2.3%
Other values (14) 16
18.6%
Arabic
ValueCountFrequency (%)
ب 3
12.5%
ل 3
12.5%
ى 2
 
8.3%
ي 2
 
8.3%
ح 2
 
8.3%
ف 2
 
8.3%
ة 1
 
4.2%
ج 1
 
4.2%
س 1
 
4.2%
ت 1
 
4.2%
Other values (6) 6
25.0%
Cyrillic
ValueCountFrequency (%)
а 3
20.0%
у 2
13.3%
в 2
13.3%
т 1
 
6.7%
е 1
 
6.7%
л 1
 
6.7%
к 1
 
6.7%
ы 1
 
6.7%
з 1
 
6.7%
м 1
 
6.7%
CJK
ValueCountFrequency (%)
2
 
5.7%
2
 
5.7%
2
 
5.7%
1
 
2.9%
1
 
2.9%
1
 
2.9%
1
 
2.9%
1
 
2.9%
1
 
2.9%
1
 
2.9%
Other values (22) 22
62.9%
Hangul
ValueCountFrequency (%)
1
50.0%
1
50.0%
Hiragana
ValueCountFrequency (%)
1
100.0%

release_date
Real number (ℝ)

HIGH CORRELATION 

Distinct70
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1990.2369
Minimum1950
Maximum2019
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size221.8 KiB
2024-01-18T20:04:32.066629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1950
5-th percentile1959
Q11975
median1991
Q32007
95-th percentile2017
Maximum2019
Range69
Interquartile range (IQR)32

Descriptive statistics

Standard deviation18.487463
Coefficient of variation (CV)0.0092890767
Kurtosis-1.1017566
Mean1990.2369
Median Absolute Deviation (MAD)16
Skewness-0.17848982
Sum56467001
Variance341.78629
MonotonicityNot monotonic
2024-01-18T20:04:32.243984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2017 660
 
2.3%
2018 653
 
2.3%
2015 617
 
2.2%
2009 597
 
2.1%
2012 588
 
2.1%
2013 560
 
2.0%
2014 537
 
1.9%
2019 534
 
1.9%
2016 533
 
1.9%
2011 531
 
1.9%
Other values (60) 22562
79.5%
ValueCountFrequency (%)
1950 51
 
0.2%
1951 58
 
0.2%
1952 60
 
0.2%
1953 48
 
0.2%
1954 109
 
0.4%
1955 106
 
0.4%
1956 200
0.7%
1957 237
0.8%
1958 287
1.0%
1959 312
1.1%
ValueCountFrequency (%)
2019 534
1.9%
2018 653
2.3%
2017 660
2.3%
2016 533
1.9%
2015 617
2.2%
2014 537
1.9%
2013 560
2.0%
2012 588
2.1%
2011 531
1.9%
2010 418
1.5%

genre
Categorical

HIGH CORRELATION 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size221.8 KiB
pop
7042 
country
5445 
blues
4604 
rock
4034 
jazz
3845 
Other values (2)
3402 

Length

Max length7
Median length6
Mean length4.7614902
Min length3

Characters and Unicode

Total characters135093
Distinct characters20
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowpop
2nd rowpop
3rd rowpop
4th rowpop
5th rowpop

Common Values

ValueCountFrequency (%)
pop 7042
24.8%
country 5445
19.2%
blues 4604
16.2%
rock 4034
14.2%
jazz 3845
13.6%
reggae 2498
 
8.8%
hip hop 904
 
3.2%

Length

2024-01-18T20:04:32.417907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-18T20:04:32.738637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
pop 7042
24.1%
country 5445
18.6%
blues 4604
15.7%
rock 4034
13.8%
jazz 3845
13.1%
reggae 2498
 
8.5%
hip 904
 
3.1%
hop 904
 
3.1%

Most occurring characters

ValueCountFrequency (%)
o 17425
12.9%
p 15892
11.8%
r 11977
 
8.9%
u 10049
 
7.4%
e 9600
 
7.1%
c 9479
 
7.0%
z 7690
 
5.7%
a 6343
 
4.7%
t 5445
 
4.0%
n 5445
 
4.0%
Other values (10) 35748
26.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 134189
99.3%
Space Separator 904
 
0.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 17425
13.0%
p 15892
11.8%
r 11977
 
8.9%
u 10049
 
7.5%
e 9600
 
7.2%
c 9479
 
7.1%
z 7690
 
5.7%
a 6343
 
4.7%
t 5445
 
4.1%
n 5445
 
4.1%
Other values (9) 34844
26.0%
Space Separator
ValueCountFrequency (%)
904
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 134189
99.3%
Common 904
 
0.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 17425
13.0%
p 15892
11.8%
r 11977
 
8.9%
u 10049
 
7.5%
e 9600
 
7.2%
c 9479
 
7.1%
z 7690
 
5.7%
a 6343
 
4.7%
t 5445
 
4.1%
n 5445
 
4.1%
Other values (9) 34844
26.0%
Common
ValueCountFrequency (%)
904
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 135093
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 17425
12.9%
p 15892
11.8%
r 11977
 
8.9%
u 10049
 
7.4%
e 9600
 
7.1%
c 9479
 
7.0%
z 7690
 
5.7%
a 6343
 
4.7%
t 5445
 
4.0%
n 5445
 
4.0%
Other values (10) 35748
26.5%

lyrics
Text

UNIQUE 

Distinct28372
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size221.8 KiB
2024-01-18T20:04:32.995356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length1714
Median length1061
Mean length446.90836
Min length4

Characters and Unicode

Total characters12679684
Distinct characters670
Distinct categories5 ?
Distinct scripts9 ?
Distinct blocks11 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique28372 ?
Unique (%)100.0%

Sample

1st rowhold time feel break feel untrue convince speak voice tear try hold hurt try forgive okay play break string feel heart want feel tell real truth hurt lie worse anymore little turn dust play house ruin run leave save like chase train late late tear try hold hurt try forgive okay play break string feel heart want feel tell real truth hurt lie worse anymore little run leave save like chase train know late late play break string feel heart want feel tell real truth hurt lie worse anymore little know little hold time feel
2nd rowbelieve drop rain fall grow believe darkest night candle glow believe go astray come believe believe believe smallest prayer hear believe great hear word time hear bear baby touch leaf believe believe believe lord heaven guide sin hide believe calvary die pierce believe death rise meet heaven loud amen know believe
3rd rowsweetheart send letter goodbye secret feel better wake dream think real false emotions feel heartaches hang long blue get bluer song remember cloudy hair
4th rowkiss lips want stroll charm mambo chacha meringue heaven arm japan brag geisha care long uncle eye starry sort gleam like million dollar dream come true everybody wish steal heart away guess try eye starry sort gleam like million dollar dream come true kiss lips want stroll charm japan brag geisha care long uncle
5th rowtill darling till matter know till dream live apart know hearts till world free oceans cross mountains climb know gain loss pray loss time till dream till memory till hold till oceans cross mountains climb know gain loss pray loss time till dream till memory till hold till
ValueCountFrequency (%)
know 33526
 
1.6%
like 29649
 
1.4%
time 26504
 
1.3%
come 23619
 
1.1%
go 17032
 
0.8%
feel 16795
 
0.8%
yeah 16787
 
0.8%
away 16773
 
0.8%
heart 16737
 
0.8%
life 15906
 
0.8%
Other values (51683) 1858635
89.7%
2024-01-18T20:04:33.455014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2043591
16.1%
e 1319931
 
10.4%
a 890830
 
7.0%
o 745010
 
5.9%
i 724912
 
5.7%
t 719674
 
5.7%
r 705211
 
5.6%
n 691191
 
5.5%
l 682254
 
5.4%
s 593524
 
4.7%
Other values (660) 3563556
28.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 10634085
83.9%
Space Separator 2043591
 
16.1%
Other Letter 1986
 
< 0.1%
Modifier Letter 17
 
< 0.1%
Other Number 5
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
ا 40
 
2.0%
ي 34
 
1.7%
33
 
1.7%
و 29
 
1.5%
29
 
1.5%
29
 
1.5%
ل 28
 
1.4%
22
 
1.1%
22
 
1.1%
22
 
1.1%
Other values (577) 1698
85.5%
Lowercase Letter
ValueCountFrequency (%)
e 1319931
12.4%
a 890830
 
8.4%
o 745010
 
7.0%
i 724912
 
6.8%
t 719674
 
6.8%
r 705211
 
6.6%
n 691191
 
6.5%
l 682254
 
6.4%
s 593524
 
5.6%
h 423037
 
4.0%
Other values (69) 3138511
29.5%
Modifier Letter
ValueCountFrequency (%)
ʼ 13
76.5%
4
 
23.5%
Space Separator
ValueCountFrequency (%)
2043591
100.0%
Other Number
ValueCountFrequency (%)
½ 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 10634054
83.9%
Common 2043614
 
16.1%
Hangul 738
 
< 0.1%
Han 598
 
< 0.1%
Hiragana 294
 
< 0.1%
Arabic 290
 
< 0.1%
Katakana 62
 
< 0.1%
Cyrillic 30
 
< 0.1%
Tamil 4
 
< 0.1%

Most frequent character per script

Han
ValueCountFrequency (%)
29
 
4.8%
22
 
3.7%
16
 
2.7%
15
 
2.5%
10
 
1.7%
8
 
1.3%
7
 
1.2%
7
 
1.2%
7
 
1.2%
6
 
1.0%
Other values (255) 471
78.8%
Hangul
ValueCountFrequency (%)
33
 
4.5%
29
 
3.9%
21
 
2.8%
21
 
2.8%
20
 
2.7%
16
 
2.2%
15
 
2.0%
15
 
2.0%
14
 
1.9%
14
 
1.9%
Other values (192) 540
73.2%
Latin
ValueCountFrequency (%)
e 1319931
12.4%
a 890830
 
8.4%
o 745010
 
7.0%
i 724912
 
6.8%
t 719674
 
6.8%
r 705211
 
6.6%
n 691191
 
6.5%
l 682254
 
6.4%
s 593524
 
5.6%
h 423037
 
4.0%
Other values (51) 3138480
29.5%
Hiragana
ValueCountFrequency (%)
22
 
7.5%
22
 
7.5%
14
 
4.8%
13
 
4.4%
13
 
4.4%
13
 
4.4%
12
 
4.1%
10
 
3.4%
10
 
3.4%
10
 
3.4%
Other values (43) 155
52.7%
Katakana
ValueCountFrequency (%)
5
 
8.1%
5
 
8.1%
4
 
6.5%
4
 
6.5%
3
 
4.8%
3
 
4.8%
3
 
4.8%
2
 
3.2%
2
 
3.2%
2
 
3.2%
Other values (26) 29
46.8%
Arabic
ValueCountFrequency (%)
ا 40
13.8%
ي 34
11.7%
و 29
 
10.0%
ل 28
 
9.7%
م 18
 
6.2%
ك 14
 
4.8%
س 12
 
4.1%
ب 12
 
4.1%
ت 11
 
3.8%
ر 11
 
3.8%
Other values (18) 81
27.9%
Cyrillic
ValueCountFrequency (%)
е 4
13.3%
я 4
13.3%
а 3
10.0%
о 2
 
6.7%
т 2
 
6.7%
и 2
 
6.7%
у 2
 
6.7%
л 2
 
6.7%
щ 1
 
3.3%
п 1
 
3.3%
Other values (7) 7
23.3%
Common
ValueCountFrequency (%)
2043591
> 99.9%
ʼ 13
 
< 0.1%
½ 5
 
< 0.1%
4
 
< 0.1%
µ 1
 
< 0.1%
Tamil
ValueCountFrequency (%)
2
50.0%
1
25.0%
1
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12676770
> 99.9%
None 880
 
< 0.1%
Hangul 738
 
< 0.1%
CJK 598
 
< 0.1%
Hiragana 294
 
< 0.1%
Arabic 290
 
< 0.1%
Katakana 66
 
< 0.1%
Cyrillic 30
 
< 0.1%
Modifier Letters 13
 
< 0.1%
Tamil 4
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2043591
16.1%
e 1319931
 
10.4%
a 890830
 
7.0%
o 745010
 
5.9%
i 724912
 
5.7%
t 719674
 
5.7%
r 705211
 
5.6%
n 691191
 
5.5%
l 682254
 
5.4%
s 593524
 
4.7%
Other values (17) 3560642
28.1%
None
ValueCountFrequency (%)
é 289
32.8%
í 79
 
9.0%
ó 69
 
7.8%
á 69
 
7.8%
ñ 53
 
6.0%
â 48
 
5.5%
ö 37
 
4.2%
è 36
 
4.1%
ä 23
 
2.6%
à 21
 
2.4%
Other values (26) 156
17.7%
Arabic
ValueCountFrequency (%)
ا 40
13.8%
ي 34
11.7%
و 29
 
10.0%
ل 28
 
9.7%
م 18
 
6.2%
ك 14
 
4.8%
س 12
 
4.1%
ب 12
 
4.1%
ت 11
 
3.8%
ر 11
 
3.8%
Other values (18) 81
27.9%
Hangul
ValueCountFrequency (%)
33
 
4.5%
29
 
3.9%
21
 
2.8%
21
 
2.8%
20
 
2.7%
16
 
2.2%
15
 
2.0%
15
 
2.0%
14
 
1.9%
14
 
1.9%
Other values (192) 540
73.2%
CJK
ValueCountFrequency (%)
29
 
4.8%
22
 
3.7%
16
 
2.7%
15
 
2.5%
10
 
1.7%
8
 
1.3%
7
 
1.2%
7
 
1.2%
7
 
1.2%
6
 
1.0%
Other values (255) 471
78.8%
Hiragana
ValueCountFrequency (%)
22
 
7.5%
22
 
7.5%
14
 
4.8%
13
 
4.4%
13
 
4.4%
13
 
4.4%
12
 
4.1%
10
 
3.4%
10
 
3.4%
10
 
3.4%
Other values (43) 155
52.7%
Modifier Letters
ValueCountFrequency (%)
ʼ 13
100.0%
Katakana
ValueCountFrequency (%)
5
 
7.6%
5
 
7.6%
4
 
6.1%
4
 
6.1%
4
 
6.1%
3
 
4.5%
3
 
4.5%
3
 
4.5%
2
 
3.0%
2
 
3.0%
Other values (27) 31
47.0%
Cyrillic
ValueCountFrequency (%)
е 4
13.3%
я 4
13.3%
а 3
10.0%
о 2
 
6.7%
т 2
 
6.7%
и 2
 
6.7%
у 2
 
6.7%
л 2
 
6.7%
щ 1
 
3.3%
п 1
 
3.3%
Other values (7) 7
23.3%
Tamil
ValueCountFrequency (%)
2
50.0%
1
25.0%
1
25.0%
Alphabetic PF
ValueCountFrequency (%)
1
100.0%

len
Real number (ℝ)

Distinct199
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean73.028444
Minimum1
Maximum199
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size221.8 KiB
2024-01-18T20:04:33.629246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile23
Q142
median63
Q393
95-th percentile165
Maximum199
Range198
Interquartile range (IQR)51

Descriptive statistics

Standard deviation41.829831
Coefficient of variation (CV)0.5727882
Kurtosis0.57864027
Mean73.028444
Median Absolute Deviation (MAD)24
Skewness1.03586
Sum2071963
Variance1749.7347
MonotonicityNot monotonic
2024-01-18T20:04:33.808475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
44 394
 
1.4%
56 371
 
1.3%
50 370
 
1.3%
49 370
 
1.3%
37 367
 
1.3%
43 362
 
1.3%
46 358
 
1.3%
42 354
 
1.2%
39 354
 
1.2%
60 346
 
1.2%
Other values (189) 24726
87.1%
ValueCountFrequency (%)
1 6
 
< 0.1%
2 11
 
< 0.1%
3 13
 
< 0.1%
4 21
0.1%
5 11
 
< 0.1%
6 23
0.1%
7 37
0.1%
8 29
0.1%
9 32
0.1%
10 32
0.1%
ValueCountFrequency (%)
199 46
0.2%
198 30
0.1%
197 42
0.1%
196 35
0.1%
195 42
0.1%
194 35
0.1%
193 35
0.1%
192 44
0.2%
191 31
0.1%
190 38
0.1%

dating
Real number (ℝ)

Distinct27918
Distinct (%)98.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.021111633
Minimum0.00029078221
Maximum0.64770568
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size221.8 KiB
2024-01-18T20:04:33.980662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.00029078221
5-th percentile0.0005425936
Q10.00092336106
median0.0014619884
Q30.004048583
95-th percentile0.12454038
Maximum0.64770568
Range0.6474149
Interquartile range (IQR)0.003125222

Descriptive statistics

Standard deviation0.052369926
Coefficient of variation (CV)2.4806194
Kurtosis20.997205
Mean0.021111633
Median Absolute Deviation (MAD)0.00072069854
Skewness4.0366831
Sum598.97924
Variance0.0027426091
MonotonicityNot monotonic
2024-01-18T20:04:34.147197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.002631578947 9
 
< 0.1%
0.002288329519 8
 
< 0.1%
0.02631578947 8
 
< 0.1%
0.005263157895 8
 
< 0.1%
0.00350877193 7
 
< 0.1%
0.01754385965 7
 
< 0.1%
0.003759398496 6
 
< 0.1%
0.003289473684 6
 
< 0.1%
0.004048582996 6
 
< 0.1%
0.004784688995 6
 
< 0.1%
Other values (27908) 28301
99.7%
ValueCountFrequency (%)
0.0002907822077 1
< 0.1%
0.000290782208 1
< 0.1%
0.0002923976661 1
< 0.1%
0.0002973535537 1
< 0.1%
0.0002990430659 1
< 0.1%
0.0003095975399 1
< 0.1%
0.0003095975438 1
< 0.1%
0.0003114294676 1
< 0.1%
0.0003151591575 1
< 0.1%
0.0003151591665 1
< 0.1%
ValueCountFrequency (%)
0.6477056837 1
< 0.1%
0.579519711 1
< 0.1%
0.5743965223 1
< 0.1%
0.5543998463 1
< 0.1%
0.5523156343 1
< 0.1%
0.542628148 1
< 0.1%
0.537593985 1
< 0.1%
0.5374171477 1
< 0.1%
0.5357473396 1
< 0.1%
0.5353983362 1
< 0.1%

violence
Real number (ℝ)

Distinct28189
Distinct (%)99.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.11839643
Minimum0.00028449504
Maximum0.98178138
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size221.8 KiB
2024-01-18T20:04:34.318619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.00028449504
5-th percentile0.0005720824
Q10.0011198209
median0.0025062658
Q30.19260771
95-th percentile0.48640765
Maximum0.98178138
Range0.98149688
Interquartile range (IQR)0.19148789

Descriptive statistics

Standard deviation0.17868387
Coefficient of variation (CV)1.5091997
Kurtosis1.4946657
Mean0.11839643
Median Absolute Deviation (MAD)0.0019463554
Skewness1.5138478
Sum3359.1436
Variance0.031927924
MonotonicityNot monotonic
2024-01-18T20:04:34.493442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.01754385965 7
 
< 0.1%
0.02631578947 6
 
< 0.1%
0.01052631579 4
 
< 0.1%
0.01754385965 4
 
< 0.1%
0.01754385965 4
 
< 0.1%
0.2984576458 3
 
< 0.1%
0.01052631685 3
 
< 0.1%
0.001814882089 3
 
< 0.1%
0.2828768501 3
 
< 0.1%
0.002392344545 3
 
< 0.1%
Other values (28179) 28332
99.9%
ValueCountFrequency (%)
0.0002844950389 1
< 0.1%
0.0002907822232 1
< 0.1%
0.000292397673 1
< 0.1%
0.0002940311793 1
< 0.1%
0.0002973535683 1
< 0.1%
0.0002990430779 1
< 0.1%
0.0003042288128 1
< 0.1%
0.0003095975299 1
< 0.1%
0.0003095975469 1
< 0.1%
0.0003132832207 1
< 0.1%
ValueCountFrequency (%)
0.9817813761 1
< 0.1%
0.9743954476 1
< 0.1%
0.9703947359 1
< 0.1%
0.9686888242 1
< 0.1%
0.9649122798 1
< 0.1%
0.9649122767 1
< 0.1%
0.954887217 1
< 0.1%
0.947368421 1
< 0.1%
0.936842104 1
< 0.1%
0.9271255027 1
< 0.1%

world/life
Real number (ℝ)

HIGH CORRELATION 

Distinct28195
Distinct (%)99.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.12097315
Minimum0.00029078223
Maximum0.96210526
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size221.8 KiB
2024-01-18T20:04:34.665939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.00029078223
5-th percentile0.00057836903
Q10.0011695907
median0.0065789474
Q30.197793
95-th percentile0.47982057
Maximum0.96210526
Range0.96181448
Interquartile range (IQR)0.19662341

Descriptive statistics

Standard deviation0.1721996
Coefficient of variation (CV)1.4234531
Kurtosis1.2799901
Mean0.12097315
Median Absolute Deviation (MAD)0.0061771796
Skewness1.4492067
Sum3432.2501
Variance0.029652702
MonotonicityNot monotonic
2024-01-18T20:04:34.839497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.01754385965 8
 
< 0.1%
0.01754385965 6
 
< 0.1%
0.02631578947 6
 
< 0.1%
0.01052631579 4
 
< 0.1%
0.001196172288 3
 
< 0.1%
0.3408804109 3
 
< 0.1%
0.002392344608 3
 
< 0.1%
0.007518797046 3
 
< 0.1%
0.001754386021 3
 
< 0.1%
0.2105263133 3
 
< 0.1%
Other values (28185) 28330
99.9%
ValueCountFrequency (%)
0.0002907822291 1
< 0.1%
0.0002923976623 1
< 0.1%
0.0002973535634 1
< 0.1%
0.0003007519025 1
< 0.1%
0.0003059975831 1
< 0.1%
0.0003095975286 1
< 0.1%
0.0003095975395 1
< 0.1%
0.0003132832124 1
< 0.1%
0.000313283219 1
< 0.1%
0.0003132832372 1
< 0.1%
ValueCountFrequency (%)
0.9621052631 1
< 0.1%
0.9501385026 1
< 0.1%
0.9473684202 1
< 0.1%
0.9438583351 1
< 0.1%
0.9368421047 1
< 0.1%
0.9323308271 1
< 0.1%
0.930577429 1
< 0.1%
0.9147567005 1
< 0.1%
0.913875598 1
< 0.1%
0.8947368421 1
< 0.1%

night/time
Real number (ℝ)

Distinct28169
Distinct (%)99.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.057387051
Minimum0.00028918453
Maximum0.97368421
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size221.8 KiB
2024-01-18T20:04:35.013204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.00028918453
5-th percentile0.00055401667
Q10.0010319918
median0.0019493178
Q30.065842187
95-th percentile0.33114163
Maximum0.97368421
Range0.97339503
Interquartile range (IQR)0.064810195

Descriptive statistics

Standard deviation0.11192319
Coefficient of variation (CV)1.9503213
Kurtosis8.5319185
Mean0.057387051
Median Absolute Deviation (MAD)0.0012830952
Skewness2.7615979
Sum1628.1854
Variance0.012526799
MonotonicityNot monotonic
2024-01-18T20:04:35.188569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.02631578947 8
 
< 0.1%
0.01754385965 7
 
< 0.1%
0.01754385965 5
 
< 0.1%
0.01052631579 4
 
< 0.1%
0.004784688995 3
 
< 0.1%
0.001814882293 3
 
< 0.1%
0.001754385965 3
 
< 0.1%
0.002923976608 3
 
< 0.1%
0.002392344502 3
 
< 0.1%
0.008771929825 3
 
< 0.1%
Other values (28159) 28330
99.9%
ValueCountFrequency (%)
0.0002891845286 1
< 0.1%
0.0002907822267 1
< 0.1%
0.0002973535855 1
< 0.1%
0.0002990430693 1
< 0.1%
0.0003042288061 1
< 0.1%
0.000305997554 1
< 0.1%
0.0003114294852 1
< 0.1%
0.0003114294941 1
< 0.1%
0.0003132832116 1
< 0.1%
0.0003132832246 1
< 0.1%
ValueCountFrequency (%)
0.9736842104 1
< 0.1%
0.9588100669 1
< 0.1%
0.9501385025 1
< 0.1%
0.9210526314 1
< 0.1%
0.9120572002 1
< 0.1%
0.894736841 1
< 0.1%
0.8878326379 1
< 0.1%
0.8815789468 1
< 0.1%
0.8759142029 1
< 0.1%
0.8609022556 1
< 0.1%

shake the audience
Real number (ℝ)

Distinct27161
Distinct (%)95.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.017422389
Minimum0.00028449502
Maximum0.49746324
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size221.8 KiB
2024-01-18T20:04:35.360125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.00028449502
5-th percentile0.00057208238
Q10.00099304866
median0.0015948963
Q30.01000169
95-th percentile0.097268046
Maximum0.49746324
Range0.49717875
Interquartile range (IQR)0.0090086418

Descriptive statistics

Standard deviation0.040670199
Coefficient of variation (CV)2.3343641
Kurtosis23.450776
Mean0.017422389
Median Absolute Deviation (MAD)0.00079744817
Skewness4.1506512
Sum494.30801
Variance0.0016540651
MonotonicityNot monotonic
2024-01-18T20:04:35.531262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.001754385965 18
 
0.1%
0.002192982456 17
 
0.1%
0.002631578947 17
 
0.1%
0.00350877193 16
 
0.1%
0.001814882033 15
 
0.1%
0.001879699248 15
 
0.1%
0.002105263158 14
 
< 0.1%
0.002923976608 14
 
< 0.1%
0.002392344498 14
 
< 0.1%
0.001949317739 13
 
< 0.1%
Other values (27151) 28219
99.5%
ValueCountFrequency (%)
0.0002844950238 1
< 0.1%
0.0002907822124 1
< 0.1%
0.0003059975524 1
< 0.1%
0.0003095975244 1
< 0.1%
0.0003114294641 1
< 0.1%
0.00031328321 1
< 0.1%
0.0003151591558 1
< 0.1%
0.0003170577263 1
< 0.1%
0.0003189792688 1
< 0.1%
0.000320924289 1
< 0.1%
ValueCountFrequency (%)
0.4974632448 1
< 0.1%
0.485645933 1
< 0.1%
0.4653309643 1
< 0.1%
0.4600063372 1
< 0.1%
0.4509131723 1
< 0.1%
0.4502923971 1
< 0.1%
0.4424312685 1
< 0.1%
0.4370709382 1
< 0.1%
0.4332465178 1
< 0.1%
0.4303033601 1
< 0.1%

family/gospel
Real number (ℝ)

Distinct28050
Distinct (%)98.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.017045408
Minimum0.0002891845
Maximum0.54530302
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size221.8 KiB
2024-01-18T20:04:35.697680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.0002891845
5-th percentile0.00053705694
Q10.00092336115
median0.0015037594
Q30.004784689
95-th percentile0.095047623
Maximum0.54530302
Range0.54501384
Interquartile range (IQR)0.0038613279

Descriptive statistics

Standard deviation0.041965712
Coefficient of variation (CV)2.4619952
Kurtosis27.087634
Mean0.017045408
Median Absolute Deviation (MAD)0.00075187968
Skewness4.4431351
Sum483.61231
Variance0.001761121
MonotonicityNot monotonic
2024-01-18T20:04:35.870680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.02631578947 8
 
< 0.1%
0.01754385965 7
 
< 0.1%
0.01754385965 6
 
< 0.1%
0.008771929825 6
 
< 0.1%
0.004048582996 5
 
< 0.1%
0.00350877193 5
 
< 0.1%
0.002024291498 5
 
< 0.1%
0.01052631579 5
 
< 0.1%
0.003759398496 5
 
< 0.1%
0.003289473684 5
 
< 0.1%
Other values (28040) 28315
99.8%
ValueCountFrequency (%)
0.0002891845013 1
< 0.1%
0.0002907822064 1
< 0.1%
0.0002940311706 1
< 0.1%
0.000297353559 1
< 0.1%
0.0002990430658 1
< 0.1%
0.0003007519201 1
< 0.1%
0.000304228782 1
< 0.1%
0.0003059975657 1
< 0.1%
0.0003095975243 1
< 0.1%
0.0003095975246 1
< 0.1%
ValueCountFrequency (%)
0.5453030205 1
< 0.1%
0.5065789456 1
< 0.1%
0.5028731995 1
< 0.1%
0.5013160115 1
< 0.1%
0.4982943466 1
< 0.1%
0.486515715 1
< 0.1%
0.4834833762 1
< 0.1%
0.4831910727 1
< 0.1%
0.4816878521 1
< 0.1%
0.4797251136 1
< 0.1%

romantic
Real number (ℝ)

Distinct27892
Distinct (%)98.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.048680962
Minimum0.00028449502
Maximum0.94078947
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size221.8 KiB
2024-01-18T20:04:36.036218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.00028449502
5-th percentile0.00053163213
Q10.00097465887
median0.001754386
Q30.042301315
95-th percentile0.31686382
Maximum0.94078947
Range0.94050498
Interquartile range (IQR)0.041326656

Descriptive statistics

Standard deviation0.10609521
Coefficient of variation (CV)2.1793983
Kurtosis11.04026
Mean0.048680962
Median Absolute Deviation (MAD)0.0010964912
Skewness3.1461001
Sum1381.1763
Variance0.011256193
MonotonicityNot monotonic
2024-01-18T20:04:36.200478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.00350877193 8
 
< 0.1%
0.001754385965 7
 
< 0.1%
0.004048582996 7
 
< 0.1%
0.001814882033 7
 
< 0.1%
0.004048582996 7
 
< 0.1%
0.02631578947 7
 
< 0.1%
0.002392344498 7
 
< 0.1%
0.002192982456 6
 
< 0.1%
0.01754385965 6
 
< 0.1%
0.01754385965 6
 
< 0.1%
Other values (27882) 28304
99.8%
ValueCountFrequency (%)
0.0002844950229 1
< 0.1%
0.0002907822075 1
< 0.1%
0.0002923976639 1
< 0.1%
0.0002940312204 1
< 0.1%
0.0002973535549 1
< 0.1%
0.0003059975803 1
< 0.1%
0.0003095975247 1
< 0.1%
0.0003114294762 1
< 0.1%
0.0003132832098 1
< 0.1%
0.0003132832194 1
< 0.1%
ValueCountFrequency (%)
0.9407894737 1
< 0.1%
0.9323308261 1
< 0.1%
0.894736833 1
< 0.1%
0.894736832 1
< 0.1%
0.874059249 1
< 0.1%
0.8646616512 1
< 0.1%
0.8466267355 1
< 0.1%
0.8425513907 1
< 0.1%
0.8421052631 1
< 0.1%
0.8375286041 1
< 0.1%

communication
Real number (ℝ)

Distinct28192
Distinct (%)99.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.076679754
Minimum0.00029078222
Maximum0.64582937
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size221.8 KiB
2024-01-18T20:04:36.362168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.00029078222
5-th percentile0.00058479534
Q10.0011441648
median0.002631579
Q30.13213604
95-th percentile0.30852477
Maximum0.64582937
Range0.64553859
Interquartile range (IQR)0.13099188

Descriptive statistics

Standard deviation0.10953827
Coefficient of variation (CV)1.4285161
Kurtosis2.1135342
Mean0.076679754
Median Absolute Deviation (MAD)0.002065648
Skewness1.5692208
Sum2175.558
Variance0.011998632
MonotonicityNot monotonic
2024-01-18T20:04:36.535145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.02631578947 8
 
< 0.1%
0.01754385965 6
 
< 0.1%
0.01754385965 5
 
< 0.1%
0.01052631579 5
 
< 0.1%
0.004048582996 4
 
< 0.1%
0.001754385973 3
 
< 0.1%
0.00239234454 3
 
< 0.1%
0.001196172265 3
 
< 0.1%
0.001814882083 3
 
< 0.1%
0.007518796992 3
 
< 0.1%
Other values (28182) 28329
99.8%
ValueCountFrequency (%)
0.0002907822161 1
< 0.1%
0.0002923976887 1
< 0.1%
0.0002940311882 1
< 0.1%
0.0002973535877 1
< 0.1%
0.0003059975816 1
< 0.1%
0.0003095975311 1
< 0.1%
0.000309597534 1
< 0.1%
0.0003095975414 1
< 0.1%
0.0003114294797 1
< 0.1%
0.0003132832164 1
< 0.1%
ValueCountFrequency (%)
0.6458293721 1
< 0.1%
0.6404481851 1
< 0.1%
0.6335497947 1
< 0.1%
0.632100967 1
< 0.1%
0.6312097944 1
< 0.1%
0.6303209206 1
< 0.1%
0.6267250804 1
< 0.1%
0.6158338433 1
< 0.1%
0.6150642445 1
< 0.1%
0.6138297308 1
< 0.1%

obscene
Real number (ℝ)

Distinct28203
Distinct (%)99.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.097167882
Minimum0.00028918451
Maximum0.99229782
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size221.8 KiB
2024-01-18T20:04:36.717221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.00028918451
5-th percentile0.00061199515
Q10.0010526316
median0.0018148821
Q30.088765203
95-th percentile0.51450589
Maximum0.99229782
Range0.99200863
Interquartile range (IQR)0.087712572

Descriptive statistics

Standard deviation0.18130336
Coefficient of variation (CV)1.8658775
Kurtosis2.6949748
Mean0.097167882
Median Absolute Deviation (MAD)0.0010174339
Skewness1.9105091
Sum2756.8471
Variance0.032870908
MonotonicityNot monotonic
2024-01-18T20:04:36.893859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.02631578947 6
 
< 0.1%
0.01754385965 4
 
< 0.1%
0.01754385971 4
 
< 0.1%
0.002392344525 3
 
< 0.1%
0.00125313289 3
 
< 0.1%
0.001814882053 3
 
< 0.1%
0.007518797021 3
 
< 0.1%
0.01052631583 3
 
< 0.1%
0.00154798766 3
 
< 0.1%
0.001196172361 3
 
< 0.1%
Other values (28193) 28337
99.9%
ValueCountFrequency (%)
0.0002891845061 1
< 0.1%
0.0002923976685 1
< 0.1%
0.0003042287887 1
< 0.1%
0.0003095975326 1
< 0.1%
0.0003114294789 1
< 0.1%
0.0003170577169 1
< 0.1%
0.0003189792763 1
< 0.1%
0.0003209242818 1
< 0.1%
0.0003228931419 1
< 0.1%
0.0003289473842 1
< 0.1%
ValueCountFrequency (%)
0.9922978174 1
< 0.1%
0.953646916 1
< 0.1%
0.9489440037 1
< 0.1%
0.9323308261 1
< 0.1%
0.9271255027 1
< 0.1%
0.9262233307 1
< 0.1%
0.9250403596 1
< 0.1%
0.9243928927 1
< 0.1%
0.9222542555 1
< 0.1%
0.9197473316 1
< 0.1%

music
Real number (ℝ)

Distinct28177
Distinct (%)99.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.060046647
Minimum0.00028918452
Maximum0.9569378
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size221.8 KiB
2024-01-18T20:04:37.062427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.00028918452
5-th percentile0.00054259361
Q10.00097465895
median0.001814882
Q30.05510053
95-th percentile0.36530835
Maximum0.9569378
Range0.95664861
Interquartile range (IQR)0.054125872

Descriptive statistics

Standard deviation0.1233289
Coefficient of variation (CV)2.0538849
Kurtosis7.1863928
Mean0.060046647
Median Absolute Deviation (MAD)0.0011401182
Skewness2.6647105
Sum1703.6435
Variance0.015210018
MonotonicityNot monotonic
2024-01-18T20:04:37.236760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.5263157895 4
 
< 0.1%
0.3508771927 4
 
< 0.1%
0.004385964912 4
 
< 0.1%
0.01052631579 4
 
< 0.1%
0.1007447181 3
 
< 0.1%
0.001814882079 3
 
< 0.1%
0.002770083102 3
 
< 0.1%
0.01754385965 3
 
< 0.1%
0.4105263157 3
 
< 0.1%
0.001253132954 3
 
< 0.1%
Other values (28167) 28338
99.9%
ValueCountFrequency (%)
0.0002891845183 1
< 0.1%
0.0002907822269 1
< 0.1%
0.0002923977216 1
< 0.1%
0.0002973535832 1
< 0.1%
0.000299043081 1
< 0.1%
0.0003007518835 1
< 0.1%
0.0003095975268 1
< 0.1%
0.0003095975348 1
< 0.1%
0.0003132832116 1
< 0.1%
0.0003132832185 1
< 0.1%
ValueCountFrequency (%)
0.9569377978 1
< 0.1%
0.9016104088 1
< 0.1%
0.8943289955 1
< 0.1%
0.8807780903 1
< 0.1%
0.8748923116 1
< 0.1%
0.8662852602 1
< 0.1%
0.8646616513 1
< 0.1%
0.8593674865 1
< 0.1%
0.8423574582 1
< 0.1%
0.8266591652 1
< 0.1%

movement/places
Real number (ℝ)

Distinct28200
Distinct (%)99.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.047400547
Minimum0.00028449503
Maximum0.63802088
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size221.8 KiB
2024-01-18T20:04:37.408038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.00028449503
5-th percentile0.00055401664
Q10.00099304868
median0.001644737
Q30.054257587
95-th percentile0.25731324
Maximum0.63802088
Range0.63773638
Interquartile range (IQR)0.053264538

Descriptive statistics

Standard deviation0.091546534
Coefficient of variation (CV)1.9313392
Kurtosis5.5365313
Mean0.047400547
Median Absolute Deviation (MAD)0.00089285728
Skewness2.340762
Sum1344.8483
Variance0.0083807678
MonotonicityNot monotonic
2024-01-18T20:04:37.574875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.01052631579 5
 
< 0.1%
0.02631578947 5
 
< 0.1%
0.01754385965 5
 
< 0.1%
0.01754385998 4
 
< 0.1%
0.001253132865 3
 
< 0.1%
0.001547987651 3
 
< 0.1%
0.001754386234 3
 
< 0.1%
0.001754386014 3
 
< 0.1%
0.01052631601 3
 
< 0.1%
0.002392344557 3
 
< 0.1%
Other values (28190) 28335
99.9%
ValueCountFrequency (%)
0.0002844950336 1
< 0.1%
0.0002891845063 1
< 0.1%
0.0002907822176 1
< 0.1%
0.0003007518946 1
< 0.1%
0.0003059975689 1
< 0.1%
0.0003095975501 1
< 0.1%
0.0003095975524 1
< 0.1%
0.0003114294748 1
< 0.1%
0.0003114294838 1
< 0.1%
0.0003132832126 1
< 0.1%
ValueCountFrequency (%)
0.6380208763 1
< 0.1%
0.6131256108 1
< 0.1%
0.600267927 1
< 0.1%
0.5847054195 1
< 0.1%
0.5839755672 1
< 0.1%
0.5767334753 1
< 0.1%
0.5746769547 1
< 0.1%
0.5734831751 1
< 0.1%
0.5706345503 1
< 0.1%
0.5694518643 1
< 0.1%

light/visual perceptions
Real number (ℝ)

Distinct28182
Distinct (%)99.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.049004902
Minimum0.00028449503
Maximum0.66778188
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size221.8 KiB
2024-01-18T20:04:37.741375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.00028449503
5-th percentile0.00054259362
Q10.00099304873
median0.0018148822
Q30.06429598
95-th percentile0.24988811
Maximum0.66778188
Range0.66749738
Interquartile range (IQR)0.063302931

Descriptive statistics

Standard deviation0.089554461
Coefficient of variation (CV)1.8274593
Kurtosis6.3289024
Mean0.049004902
Median Absolute Deviation (MAD)0.0011401183
Skewness2.4076729
Sum1390.3671
Variance0.0080200015
MonotonicityNot monotonic
2024-01-18T20:04:38.086447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.02631578947 8
 
< 0.1%
0.01754385965 5
 
< 0.1%
0.01754385965 4
 
< 0.1%
0.01052631653 3
 
< 0.1%
0.007518797504 3
 
< 0.1%
0.1724019853 3
 
< 0.1%
0.1952855904 3
 
< 0.1%
0.002392344569 3
 
< 0.1%
0.003095975232 3
 
< 0.1%
0.001814882101 3
 
< 0.1%
Other values (28172) 28334
99.9%
ValueCountFrequency (%)
0.0002844950344 1
< 0.1%
0.0002891845074 1
< 0.1%
0.0002923976705 1
< 0.1%
0.0002940311932 1
< 0.1%
0.0002973535753 1
< 0.1%
0.0002990430971 1
< 0.1%
0.0003059975733 1
< 0.1%
0.0003095975372 1
< 0.1%
0.0003095975422 1
< 0.1%
0.0003132832152 1
< 0.1%
ValueCountFrequency (%)
0.6677818773 1
< 0.1%
0.6421170402 1
< 0.1%
0.6395848223 1
< 0.1%
0.6294252051 1
< 0.1%
0.6278127452 1
< 0.1%
0.6233442685 1
< 0.1%
0.6120869844 1
< 0.1%
0.6115882383 1
< 0.1%
0.6091857305 1
< 0.1%
0.6043716996 1
< 0.1%

family/spiritual
Real number (ℝ)

Distinct27932
Distinct (%)98.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.024150288
Minimum0.00028449502
Maximum0.61807252
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size221.8 KiB
2024-01-18T20:04:38.267107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.00028449502
5-th percentile0.00053705693
Q10.00095693794
median0.0016447369
Q30.025498209
95-th percentile0.12642394
Maximum0.61807252
Range0.61778803
Interquartile range (IQR)0.024541271

Descriptive statistics

Standard deviation0.051025333
Coefficient of variation (CV)2.112825
Kurtosis17.156305
Mean0.024150288
Median Absolute Deviation (MAD)0.00093349931
Skewness3.5826528
Sum685.19198
Variance0.0026035846
MonotonicityNot monotonic
2024-01-18T20:04:38.459246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.001754385965 9
 
< 0.1%
0.002288329519 8
 
< 0.1%
0.001879699248 8
 
< 0.1%
0.01754385965 8
 
< 0.1%
0.002631578947 7
 
< 0.1%
0.005263157895 7
 
< 0.1%
0.001253132832 6
 
< 0.1%
0.02631578947 6
 
< 0.1%
0.002392344498 6
 
< 0.1%
0.002631578947 6
 
< 0.1%
Other values (27922) 28301
99.7%
ValueCountFrequency (%)
0.0002844950238 1
< 0.1%
0.0002891845018 1
< 0.1%
0.0002907822057 1
< 0.1%
0.0002907822207 1
< 0.1%
0.0002923976625 1
< 0.1%
0.0002940311718 1
< 0.1%
0.000297353576 1
< 0.1%
0.0003007519286 1
< 0.1%
0.0003042288269 1
< 0.1%
0.0003059976296 1
< 0.1%
ValueCountFrequency (%)
0.6180725221 1
< 0.1%
0.6016174698 1
< 0.1%
0.5517260677 1
< 0.1%
0.5033835109 1
< 0.1%
0.4989978571 1
< 0.1%
0.4924043188 1
< 0.1%
0.4839028113 1
< 0.1%
0.4789254429 1
< 0.1%
0.4771943156 1
< 0.1%
0.4758166848 1
< 0.1%

like/girls
Real number (ℝ)

Distinct28094
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.028056991
Minimum0.00028449504
Maximum0.59445874
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size221.8 KiB
2024-01-18T20:04:38.645666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.00028449504
5-th percentile0.00054824568
Q10.00097465888
median0.0015948964
Q30.026621703
95-th percentile0.15165406
Maximum0.59445874
Range0.59417425
Interquartile range (IQR)0.025647045

Descriptive statistics

Standard deviation0.058472795
Coefficient of variation (CV)2.0840722
Kurtosis11.655026
Mean0.028056991
Median Absolute Deviation (MAD)0.0008430166
Skewness3.0638868
Sum796.03294
Variance0.0034190677
MonotonicityNot monotonic
2024-01-18T20:04:38.827233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.02631578947 8
 
< 0.1%
0.01754385965 5
 
< 0.1%
0.01754385965 5
 
< 0.1%
0.002288329519 5
 
< 0.1%
0.002192982456 5
 
< 0.1%
0.01754385965 5
 
< 0.1%
0.002506265664 4
 
< 0.1%
0.002631578947 4
 
< 0.1%
0.008771929825 4
 
< 0.1%
0.007518796992 4
 
< 0.1%
Other values (28084) 28323
99.8%
ValueCountFrequency (%)
0.0002844950381 1
< 0.1%
0.0002891845005 1
< 0.1%
0.0002907822193 1
< 0.1%
0.0002973535594 1
< 0.1%
0.0002990430959 1
< 0.1%
0.0003007519035 1
< 0.1%
0.0003042288155 1
< 0.1%
0.0003059975732 1
< 0.1%
0.0003095975511 1
< 0.1%
0.0003132832195 1
< 0.1%
ValueCountFrequency (%)
0.5944587414 1
< 0.1%
0.5423605939 1
< 0.1%
0.5320697548 1
< 0.1%
0.5287754237 1
< 0.1%
0.527143368 1
< 0.1%
0.500239806 1
< 0.1%
0.4995888811 1
< 0.1%
0.4994841139 1
< 0.1%
0.4955493721 1
< 0.1%
0.4954271293 1
< 0.1%

sadness
Real number (ℝ)

Distinct28191
Distinct (%)99.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.12938933
Minimum0.00028449503
Maximum0.98142415
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size221.8 KiB
2024-01-18T20:04:39.023070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.00028449503
5-th percentile0.00055991043
Q10.0011441648
median0.0052631583
Q30.23511262
95-th percentile0.49810625
Maximum0.98142415
Range0.98113965
Interquartile range (IQR)0.23396845

Descriptive statistics

Standard deviation0.18114282
Coefficient of variation (CV)1.3999826
Kurtosis0.90775761
Mean0.12938933
Median Absolute Deviation (MAD)0.004851974
Skewness1.3497767
Sum3671.0342
Variance0.03281272
MonotonicityNot monotonic
2024-01-18T20:04:39.230772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.02631578947 8
 
< 0.1%
0.01754385965 7
 
< 0.1%
0.01754385965 5
 
< 0.1%
0.002923976608 4
 
< 0.1%
0.01052631579 4
 
< 0.1%
0.001253132947 3
 
< 0.1%
0.007518797009 3
 
< 0.1%
0.2078229241 3
 
< 0.1%
0.00125313294 3
 
< 0.1%
0.0105263159 3
 
< 0.1%
Other values (28181) 28329
99.8%
ValueCountFrequency (%)
0.0002844950257 1
< 0.1%
0.0002907822237 1
< 0.1%
0.000292397673 1
< 0.1%
0.0002940311804 1
< 0.1%
0.0003042288067 1
< 0.1%
0.0003095975272 1
< 0.1%
0.0003095975358 1
< 0.1%
0.0003095975511 1
< 0.1%
0.0003132832187 1
< 0.1%
0.0003132832322 1
< 0.1%
ValueCountFrequency (%)
0.9814241477 1
< 0.1%
0.939147619 1
< 0.1%
0.9300551173 1
< 0.1%
0.925904536 1
< 0.1%
0.9252871648 1
< 0.1%
0.9210526307 1
< 0.1%
0.9210526295 1
< 0.1%
0.920456444 1
< 0.1%
0.9162304076 1
< 0.1%
0.91155954 1
< 0.1%

feelings
Real number (ℝ)

Distinct27707
Distinct (%)97.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.030996269
Minimum0.0002891845
Maximum0.95881007
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size221.8 KiB
2024-01-18T20:04:39.438676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.0002891845
5-th percentile0.00055401669
Q10.00099304874
median0.001754386
Q30.032621554
95-th percentile0.14544795
Maximum0.95881007
Range0.95852088
Interquartile range (IQR)0.031628505

Descriptive statistics

Standard deviation0.071651683
Coefficient of variation (CV)2.3116228
Kurtosis23.543925
Mean0.030996269
Median Absolute Deviation (MAD)0.0010334054
Skewness4.2796634
Sum879.42616
Variance0.0051339637
MonotonicityNot monotonic
2024-01-18T20:04:39.646224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.001879699248 11
 
< 0.1%
0.002631578947 10
 
< 0.1%
0.001949317739 9
 
< 0.1%
0.001503759398 9
 
< 0.1%
0.003289473684 9
 
< 0.1%
0.004048582996 9
 
< 0.1%
0.002506265664 9
 
< 0.1%
0.001814882033 8
 
< 0.1%
0.02631578947 8
 
< 0.1%
0.002923976608 8
 
< 0.1%
Other values (27697) 28282
99.7%
ValueCountFrequency (%)
0.0002891845002 1
< 0.1%
0.0002923976798 1
< 0.1%
0.0002973535754 1
< 0.1%
0.0002990430627 1
< 0.1%
0.0003042287901 1
< 0.1%
0.0003059975621 1
< 0.1%
0.0003095976267 1
< 0.1%
0.0003114294852 1
< 0.1%
0.0003132832082 1
< 0.1%
0.0003132832245 1
< 0.1%
ValueCountFrequency (%)
0.9588100686 1
< 0.1%
0.9210526315 1
< 0.1%
0.8209665243 1
< 0.1%
0.8140809728 1
< 0.1%
0.8063878355 1
< 0.1%
0.7772460759 1
< 0.1%
0.7748199687 1
< 0.1%
0.7566552912 1
< 0.1%
0.7480230283 1
< 0.1%
0.7297741849 1
< 0.1%

danceability
Real number (ℝ)

Distinct859
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5333481
Minimum0.005415358
Maximum0.99350157
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size221.8 KiB
2024-01-18T20:04:39.847394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.005415358
5-th percentile0.2375176
Q10.4129752
median0.5386115
Q30.65666631
95-th percentile0.81046247
Maximum0.99350157
Range0.98808621
Interquartile range (IQR)0.24369111

Descriptive statistics

Standard deviation0.17321799
Coefficient of variation (CV)0.32477474
Kurtosis-0.43913816
Mean0.5333481
Median Absolute Deviation (MAD)0.12130402
Skewness-0.11780021
Sum15132.152
Variance0.030004472
MonotonicityNot monotonic
2024-01-18T20:04:40.077636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.5115347124 82
 
0.3%
0.5451099318 82
 
0.3%
0.5960142965 81
 
0.3%
0.5862666522 81
 
0.3%
0.6577493772 81
 
0.3%
0.5570237193 81
 
0.3%
0.5321130727 80
 
0.3%
0.6003465829 80
 
0.3%
0.5689375068 79
 
0.3%
0.5245315715 79
 
0.3%
Other values (849) 27566
97.2%
ValueCountFrequency (%)
0.005415357955 1
< 0.1%
0.01440485216 1
< 0.1%
0.02187804614 1
< 0.1%
0.02523556807 1
< 0.1%
0.02935124012 1
< 0.1%
0.03433336944 1
< 0.1%
0.04072349182 1
< 0.1%
0.04083179898 1
< 0.1%
0.04364778512 1
< 0.1%
0.0458139283 2
< 0.1%
ValueCountFrequency (%)
0.9935015705 1
 
< 0.1%
0.9913354273 1
 
< 0.1%
0.9870031409 1
 
< 0.1%
0.9805047114 1
 
< 0.1%
0.9794216398 1
 
< 0.1%
0.9783385682 1
 
< 0.1%
0.976172425 3
< 0.1%
0.9718401386 1
 
< 0.1%
0.970757067 1
 
< 0.1%
0.9696739955 1
 
< 0.1%

loudness
Real number (ℝ)

HIGH CORRELATION 

Distinct13066
Distinct (%)46.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.6652492
Minimum0
Maximum1
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size221.8 KiB
2024-01-18T20:04:40.303475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.47105479
Q10.59536446
median0.67905033
Q30.74902572
95-th percentile0.81325667
Maximum1
Range1
Interquartile range (IQR)0.15366126

Descriptive statistics

Standard deviation0.10843407
Coefficient of variation (CV)0.16299767
Kurtosis0.55492187
Mean0.6652492
Median Absolute Deviation (MAD)0.075609568
Skewness-0.70856129
Sum18874.45
Variance0.011757947
MonotonicityNot monotonic
2024-01-18T20:04:40.525079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.7548137323 10
 
< 0.1%
0.7488398328 10
 
< 0.1%
0.7332512884 9
 
< 0.1%
0.7631207856 9
 
< 0.1%
0.6931518088 9
 
< 0.1%
0.6240032818 9
 
< 0.1%
0.6398482168 9
 
< 0.1%
0.6808707023 9
 
< 0.1%
0.7216880753 9
 
< 0.1%
0.7140732764 8
 
< 0.1%
Other values (13056) 28281
99.7%
ValueCountFrequency (%)
0 1
< 0.1%
0.05822629029 1
< 0.1%
0.08796759224 1
< 0.1%
0.1014024562 1
< 0.1%
0.1222213676 1
< 0.1%
0.1245288824 1
< 0.1%
0.137784273 1
< 0.1%
0.1448862908 1
< 0.1%
0.1484501192 1
< 0.1%
0.1556803323 1
< 0.1%
ValueCountFrequency (%)
1 1
< 0.1%
0.9676947927 1
< 0.1%
0.9645411891 1
< 0.1%
0.9445170884 1
< 0.1%
0.9404404789 1
< 0.1%
0.9367228162 1
< 0.1%
0.934235828 1
< 0.1%
0.9339537984 1
< 0.1%
0.93228726 1
< 0.1%
0.92172397 1
< 0.1%

acousticness
Real number (ℝ)

HIGH CORRELATION 

Distinct3786
Distinct (%)13.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.33923473
Minimum2.8112478 × 10-7
Maximum1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size221.8 KiB
2024-01-18T20:04:40.742768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.8112478 × 10-7
5-th percentile0.00044432776
Q10.034235978
median0.22590284
Q30.63252975
95-th percentile0.92570274
Maximum1
Range0.99999972
Interquartile range (IQR)0.59829377

Descriptive statistics

Standard deviation0.32671429
Coefficient of variation (CV)0.96309211
Kurtosis-1.1266023
Mean0.33923473
Median Absolute Deviation (MAD)0.21812271
Skewness0.58323692
Sum9624.7678
Variance0.10674223
MonotonicityNot monotonic
2024-01-18T20:04:40.994371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1174689934 51
 
0.2%
0.1214850617 48
 
0.2%
0.1044167715 47
 
0.2%
0.1194770276 44
 
0.2%
0.1234930959 44
 
0.2%
0.1134529252 44
 
0.2%
0.1204810447 44
 
0.2%
0.1074288227 42
 
0.1%
0.1144569422 41
 
0.1%
0.1054207886 41
 
0.1%
Other values (3776) 27926
98.4%
ValueCountFrequency (%)
2.811247802 × 10-71
< 0.1%
3.012051217 × 10-71
< 0.1%
3.714863168 × 10-72
< 0.1%
4.317273411 × 10-71
< 0.1%
5.522093898 × 10-71
< 0.1%
6.52611097 × 10-71
< 0.1%
7.028119506 × 10-71
< 0.1%
7.329324628 × 10-71
< 0.1%
7.831333164 × 10-71
< 0.1%
8.3333417 × 10-71
< 0.1%
ValueCountFrequency (%)
1 3
 
< 0.1%
0.9989959829 17
0.1%
0.9979919659 16
0.1%
0.9969879488 18
0.1%
0.9959839317 17
0.1%
0.9949799146 19
0.1%
0.9939758976 11
< 0.1%
0.9929718805 24
0.1%
0.9919678634 20
0.1%
0.9909638463 17
0.1%

instrumentalness
Real number (ℝ)

ZEROS 

Distinct4939
Distinct (%)17.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.080048954
Minimum0
Maximum0.99696356
Zeros7870
Zeros (%)27.7%
Negative0
Negative (%)0.0%
Memory size221.8 KiB
2024-01-18T20:04:41.237517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median8.4919028 × 10-5
Q30.0093345142
95-th percentile0.70040486
Maximum0.99696356
Range0.99696356
Interquartile range (IQR)0.0093345142

Descriptive statistics

Standard deviation0.21124533
Coefficient of variation (CV)2.6389518
Kurtosis7.3185295
Mean0.080048954
Median Absolute Deviation (MAD)8.4919028 × 10-5
Skewness2.9015825
Sum2271.1489
Variance0.044624589
MonotonicityNot monotonic
2024-01-18T20:04:41.492616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 7870
 
27.7%
1.093117409 × 10-524
 
0.1%
0.0001234817814 23
 
0.1%
0.001255060729 21
 
0.1%
0.1052631579 21
 
0.1%
0.001578947368 20
 
0.1%
0.01255060729 20
 
0.1%
0.0001032388664 20
 
0.1%
0.0001082995951 20
 
0.1%
0.001143724696 19
 
0.1%
Other values (4929) 20314
71.6%
ValueCountFrequency (%)
0 7870
27.7%
1.012145749 × 10-66
 
< 0.1%
1.022267206 × 10-613
 
< 0.1%
1.032388664 × 10-617
 
0.1%
1.042510121 × 10-66
 
< 0.1%
1.052631579 × 10-611
 
< 0.1%
1.062753036 × 10-612
 
< 0.1%
1.072874494 × 10-69
 
< 0.1%
1.082995951 × 10-67
 
< 0.1%
1.093117409 × 10-69
 
< 0.1%
ValueCountFrequency (%)
0.9969635628 1
< 0.1%
0.9939271255 1
< 0.1%
0.991902834 1
< 0.1%
0.9908906883 1
< 0.1%
0.9858299595 1
< 0.1%
0.9848178138 1
< 0.1%
0.983805668 1
< 0.1%
0.9787449393 2
< 0.1%
0.9777327935 2
< 0.1%
0.9767206478 1
< 0.1%

valence
Real number (ℝ)

Distinct1295
Distinct (%)4.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.53286378
Minimum0
Maximum1
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size221.8 KiB
2024-01-18T20:04:41.770200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.12613355
Q10.32914262
median0.53936521
Q30.73825227
95-th percentile0.9299258
Maximum1
Range1
Interquartile range (IQR)0.40910965

Descriptive statistics

Standard deviation0.2509719
Coefficient of variation (CV)0.47098697
Kurtosis-1.0283628
Mean0.53286378
Median Absolute Deviation (MAD)0.20507007
Skewness-0.070639891
Sum15118.411
Variance0.062986895
MonotonicityNot monotonic
2024-01-18T20:04:42.029896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.9670239077 90
 
0.3%
0.9680544106 89
 
0.3%
0.9659934048 89
 
0.3%
0.9690849134 70
 
0.2%
0.9649629019 59
 
0.2%
0.9701154163 59
 
0.2%
0.7382522671 54
 
0.2%
0.5847073372 52
 
0.2%
0.5754328112 51
 
0.2%
0.5620362737 51
 
0.2%
Other values (1285) 27708
97.7%
ValueCountFrequency (%)
0 1
< 0.1%
0.01020197857 1
< 0.1%
0.01030502885 1
< 0.1%
0.0108202803 1
< 0.1%
0.01123248145 1
< 0.1%
0.01154163232 1
< 0.1%
0.01174773289 1
< 0.1%
0.01195383347 1
< 0.1%
0.01215993405 1
< 0.1%
0.01236603462 1
< 0.1%
ValueCountFrequency (%)
1 1
 
< 0.1%
0.9969084913 2
< 0.1%
0.9917559769 1
 
< 0.1%
0.990725474 1
 
< 0.1%
0.9896949711 1
 
< 0.1%
0.9886644683 1
 
< 0.1%
0.9866034625 3
< 0.1%
0.9855729596 4
< 0.1%
0.9845424567 3
< 0.1%
0.9835119538 4
< 0.1%

energy
Real number (ℝ)

HIGH CORRELATION 

Distinct1348
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.569875
Minimum0
Maximum1
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size221.8 KiB
2024-01-18T20:04:42.269380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.15412774
Q10.38036103
median0.58056748
Q30.77276568
95-th percentile0.9409391
Maximum1
Range1
Interquartile range (IQR)0.39240465

Descriptive statistics

Standard deviation0.24438497
Coefficient of variation (CV)0.42883961
Kurtosis-0.94302544
Mean0.569875
Median Absolute Deviation (MAD)0.19620232
Skewness-0.17714716
Sum16168.493
Variance0.059724015
MonotonicityNot monotonic
2024-01-18T20:04:42.512479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.8278224505 57
 
0.2%
0.7487409016 53
 
0.2%
0.6676572882 52
 
0.2%
0.4934776742 51
 
0.2%
0.7307223209 51
 
0.2%
0.723715095 51
 
0.2%
0.9059029671 50
 
0.2%
0.7016923852 49
 
0.2%
0.4584415449 49
 
0.2%
0.7147058046 49
 
0.2%
Other values (1338) 27860
98.2%
ValueCountFrequency (%)
0 1
< 0.1%
0.003812731689 1
< 0.1%
0.004343278789 1
< 0.1%
0.004473412983 1
< 0.1%
0.007056076226 1
< 0.1%
0.007316344615 1
< 0.1%
0.007556592358 1
< 0.1%
0.007736778166 1
< 0.1%
0.00835741817 1
< 0.1%
0.008437500751 1
< 0.1%
ValueCountFrequency (%)
1 2
 
< 0.1%
0.9989989677 7
 
< 0.1%
0.9979979355 11
< 0.1%
0.9969969032 14
< 0.1%
0.9959958709 25
0.1%
0.9949948387 17
0.1%
0.9939938064 10
 
< 0.1%
0.9929927741 9
 
< 0.1%
0.9919917419 23
0.1%
0.9909907096 18
0.1%

topic
Categorical

HIGH CORRELATION 

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size221.8 KiB
sadness
6096 
violence
5710 
world/life
5420 
obscene
4882 
music
2303 
Other values (3)
3961 

Length

Max length10
Median length8
Mean length7.8802693
Min length5

Characters and Unicode

Total characters223579
Distinct characters20
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowsadness
2nd rowworld/life
3rd rowmusic
4th rowromantic
5th rowromantic

Common Values

ValueCountFrequency (%)
sadness 6096
21.5%
violence 5710
20.1%
world/life 5420
19.1%
obscene 4882
17.2%
music 2303
 
8.1%
night/time 1825
 
6.4%
romantic 1524
 
5.4%
feelings 612
 
2.2%

Length

2024-01-18T20:04:42.723427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-18T20:04:42.922296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
sadness 6096
21.5%
violence 5710
20.1%
world/life 5420
19.1%
obscene 4882
17.2%
music 2303
 
8.1%
night/time 1825
 
6.4%
romantic 1524
 
5.4%
feelings 612
 
2.2%

Most occurring characters

ValueCountFrequency (%)
e 35749
16.0%
s 26085
11.7%
n 20649
9.2%
i 19219
8.6%
o 17536
 
7.8%
l 17162
 
7.7%
c 14419
 
6.4%
d 11516
 
5.2%
a 7620
 
3.4%
/ 7245
 
3.2%
Other values (10) 46379
20.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 216334
96.8%
Other Punctuation 7245
 
3.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 35749
16.5%
s 26085
12.1%
n 20649
9.5%
i 19219
8.9%
o 17536
8.1%
l 17162
7.9%
c 14419
 
6.7%
d 11516
 
5.3%
a 7620
 
3.5%
r 6944
 
3.2%
Other values (9) 39435
18.2%
Other Punctuation
ValueCountFrequency (%)
/ 7245
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 216334
96.8%
Common 7245
 
3.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 35749
16.5%
s 26085
12.1%
n 20649
9.5%
i 19219
8.9%
o 17536
8.1%
l 17162
7.9%
c 14419
 
6.7%
d 11516
 
5.3%
a 7620
 
3.5%
r 6944
 
3.2%
Other values (9) 39435
18.2%
Common
ValueCountFrequency (%)
/ 7245
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 223579
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 35749
16.0%
s 26085
11.7%
n 20649
9.2%
i 19219
8.6%
o 17536
 
7.8%
l 17162
 
7.7%
c 14419
 
6.4%
d 11516
 
5.2%
a 7620
 
3.4%
/ 7245
 
3.2%
Other values (10) 46379
20.7%

age
Real number (ℝ)

HIGH CORRELATION 

Distinct70
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.42518731
Minimum0.014285714
Maximum1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size221.8 KiB
2024-01-18T20:04:43.156720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.014285714
5-th percentile0.042857143
Q10.18571429
median0.41428571
Q30.64285714
95-th percentile0.87142857
Maximum1
Range0.98571429
Interquartile range (IQR)0.45714286

Descriptive statistics

Standard deviation0.26410662
Coefficient of variation (CV)0.62115357
Kurtosis-1.1017566
Mean0.42518731
Median Absolute Deviation (MAD)0.22857143
Skewness0.17848982
Sum12063.414
Variance0.069752304
MonotonicityNot monotonic
2024-01-18T20:04:43.392333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.04285714286 660
 
2.3%
0.02857142857 653
 
2.3%
0.07142857143 617
 
2.2%
0.1571428571 597
 
2.1%
0.1142857143 588
 
2.1%
0.1 560
 
2.0%
0.08571428571 537
 
1.9%
0.01428571429 534
 
1.9%
0.05714285714 533
 
1.9%
0.1285714286 531
 
1.9%
Other values (60) 22562
79.5%
ValueCountFrequency (%)
0.01428571429 534
1.9%
0.02857142857 653
2.3%
0.04285714286 660
2.3%
0.05714285714 533
1.9%
0.07142857143 617
2.2%
0.08571428571 537
1.9%
0.1 560
2.0%
0.1142857143 588
2.1%
0.1285714286 531
1.9%
0.1428571429 418
1.5%
ValueCountFrequency (%)
1 51
 
0.2%
0.9857142857 58
 
0.2%
0.9714285714 60
 
0.2%
0.9571428571 48
 
0.2%
0.9428571429 109
 
0.4%
0.9285714286 106
 
0.4%
0.9142857143 200
0.7%
0.9 237
0.8%
0.8857142857 287
1.0%
0.8714285714 312
1.1%

Interactions

2024-01-18T20:04:26.401891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:03:06.852963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:03:09.905177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:03:12.987047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:03:16.083195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:03:19.208520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:03:22.075463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:03:24.989644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:03:28.169404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:03:31.077931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:03:33.962964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:03:37.181847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:03:40.255578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:03:43.081243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:03:46.325057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:03:49.502236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:03:52.839974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:03:56.343550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:03:59.650013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:04:02.990829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:04:06.138534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:04:09.232161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:04:12.304092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:04:15.976110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:04:19.948325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:04:23.223102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:04:26.504816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:03:06.954347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:03:10.011624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:03:13.093523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:03:16.181127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:03:19.303359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:03:22.178731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:03:25.093697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:03:28.270600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:03:31.181615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:03:34.063932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:03:37.304603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:03:40.358691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:03:43.181583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:03:46.439962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:03:49.636174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:03:52.962420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:03:56.472562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:03:59.791419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:04:03.106547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:04:06.257318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:04:09.335185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:04:12.427994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:04:16.110726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:04:20.077080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:04:23.335718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:04:26.626550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:03:07.068131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:03:10.131686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:03:13.211670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:03:16.295299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:03:19.424843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:03:22.293989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:03:25.208235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:03:28.390526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:03:31.298688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:03:34.176859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:03:37.438180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:03:40.478613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:03:43.304184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:03:46.568320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:03:49.770926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:03:53.112243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:03:56.597053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:03:59.940314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:04:03.243674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:04:06.396422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:04:09.445988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:04:12.565435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:04:16.306098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:04:20.219042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:04:23.459729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:04:26.742869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:03:07.182143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:03:10.254687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:03:13.333137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:03:16.424204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:03:19.545384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:03:22.412639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:03:25.326666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:03:28.504900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:03:31.419246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:03:34.296847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:03:37.565696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:03:40.593599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:03:43.421531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:03:46.711918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:03:49.897684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:03:53.252950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:03:56.724699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:04:00.093574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:04:03.372772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:04:06.534463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:04:09.558331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:04:12.714740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:04:16.522629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:04:20.355097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:04:23.601584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:04:26.858662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:03:07.292121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:03:10.368776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:03:13.472319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:03:16.551870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:03:19.656422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:03:22.533329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:03:25.447184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
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2024-01-18T20:04:02.143633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:04:05.348097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:04:08.471887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:04:11.382717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:04:14.885322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:04:18.770014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:04:22.419362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:04:25.646556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:04:28.635351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:03:09.271523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:03:12.295294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:03:15.376198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:03:18.578537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:03:21.402909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:03:24.295937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:03:27.490285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:03:30.393395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:03:33.323022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:03:36.476134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:03:39.598435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:03:42.447615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:03:45.613893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:03:48.781630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:03:52.092783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:03:55.593569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:03:58.821744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:04:02.268825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:04:05.474628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:04:08.589528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:04:11.515733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:04:15.016770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:04:18.911055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:04:22.534953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:04:25.754732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:04:28.744657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:03:09.378947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:03:12.410945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:03:15.499837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:03:18.686732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:03:21.507990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:03:24.403190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:03:27.606005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:03:30.506884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:03:33.429768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:03:36.583993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:03:39.709483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:03:42.551708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:03:45.721173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:03:48.898742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:03:52.224581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:03:55.710400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:03:58.973985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:04:02.385400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:04:05.587690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:04:08.705356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:04:11.656421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:04:15.135635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:04:19.059877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:04:22.650954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:04:25.860419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:04:28.854042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:03:09.483795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:03:12.527028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:03:15.615520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:03:18.788559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:03:21.614688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:03:24.513454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:03:27.724440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:03:30.614771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:03:33.535633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:03:36.700773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:03:39.816763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:03:42.659781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:03:45.829319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:03:49.009788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:03:52.354326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:03:55.826820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:03:59.118262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:04:02.493071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:04:05.703887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:04:08.810536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:04:11.785934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:04:15.277072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:04:19.414561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:04:22.770412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:04:25.969712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:04:28.961496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:03:09.589544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:03:12.645901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:03:15.727629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:03:18.897385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:03:21.729432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:03:24.632760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:03:27.850023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:03:30.726867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:03:33.642431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:03:36.824784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:03:39.926511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:03:42.766636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:03:45.954166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:03:49.137102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:03:52.477386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:03:55.957801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:03:59.257275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:04:02.602811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:04:05.815848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:04:08.916508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:04:11.908382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:04:15.436307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:04:19.537820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:04:22.884385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:04:26.075796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:04:29.068734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:03:09.694586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:03:12.755472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:03:15.855018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:03:19.003438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:03:21.850901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:03:24.759754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:03:27.962147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:03:30.846363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:03:33.752764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:03:36.951277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:03:40.038733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:03:42.874034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:03:46.085371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:03:49.267553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:03:52.596526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:03:56.083066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:03:59.387161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:04:02.743037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:04:05.924548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:04:09.020155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:04:12.031392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:04:15.588306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:04:19.674712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:04:23.003745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:04:26.183851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:04:29.175603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:03:09.798036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:03:12.876421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:03:15.973356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:03:19.105208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:03:21.968209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:03:24.878142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:03:28.064726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:03:30.961295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:03:33.857910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:03:37.066131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:03:40.144938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:03:42.978458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:03:46.213121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:03:49.382905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:03:52.726768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:03:56.218814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:03:59.523956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:04:02.867085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:04:06.033915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:04:09.124049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:04:12.165123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:04:15.754516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:04:19.808528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:04:23.114781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-18T20:04:26.291774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-01-18T20:04:43.591983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Unnamed: 0acousticnessagecommunicationdanceabilitydatingenergyfamily/gospelfamily/spiritualfeelingsgenreinstrumentalnesslenlight/visual perceptionslike/girlsloudnessmovement/placesmusicnight/timeobscenerelease_dateromanticsadnessshake the audiencetopicvalenceviolenceworld/life
Unnamed: 01.000-0.205-0.343-0.0440.083-0.0500.172-0.0070.053-0.0140.7540.1270.0710.008-0.0250.149-0.007-0.033-0.0180.0900.343-0.091-0.0890.0300.101-0.0270.1440.012
acousticness-0.2051.0000.4030.061-0.0340.122-0.7170.1300.0800.0750.159-0.083-0.2250.1030.080-0.5400.0710.1880.050-0.039-0.4030.2190.114-0.0010.107-0.111-0.1450.063
age-0.3430.4031.0000.054-0.1700.151-0.3700.1440.1270.0980.1490.046-0.2720.1090.081-0.5020.0770.1810.086-0.076-1.0000.2180.1000.0140.1150.105-0.0750.078
communication-0.0440.0610.0541.000-0.0440.093-0.0720.0830.0580.1080.0280.012-0.258-0.0270.073-0.054-0.0480.0420.028-0.011-0.0540.1120.0660.0590.030-0.0430.005-0.005
danceability0.083-0.034-0.170-0.0441.000-0.0670.002-0.075-0.099-0.0760.205-0.1340.256-0.135-0.0490.022-0.050-0.113-0.0660.1450.170-0.142-0.151-0.0320.1120.492-0.131-0.103
dating-0.0500.1220.1510.093-0.0671.000-0.1110.2650.1470.2190.0410.077-0.4620.1250.271-0.1040.1260.1800.2020.130-0.1510.2940.1330.2380.045-0.014-0.0130.050
energy0.172-0.717-0.370-0.0720.002-0.1111.000-0.137-0.102-0.0810.1690.0350.258-0.123-0.0890.768-0.054-0.196-0.0610.0510.370-0.219-0.1430.0000.1140.2650.124-0.109
family/gospel-0.0070.1300.1440.083-0.0750.265-0.1371.0000.2360.1760.0320.087-0.4160.1520.205-0.1390.2060.2110.1380.141-0.1440.2150.0860.1910.015-0.0330.1080.092
family/spiritual0.0530.0800.1270.058-0.0990.147-0.1020.2361.0000.1350.0370.119-0.3440.1870.128-0.1210.1570.2180.0900.029-0.1270.1590.0900.1080.046-0.0600.2110.153
feelings-0.0140.0750.0980.108-0.0760.219-0.0810.1760.1351.0000.0190.076-0.3610.1020.166-0.0800.0670.1370.1600.036-0.0980.1920.0710.1480.360-0.0350.0430.104
genre0.7540.1590.1490.0280.2050.0410.1690.0320.0370.0191.000-0.0160.122-0.023-0.0290.187-0.095-0.090-0.045-0.0290.085-0.088-0.025-0.0010.150-0.0060.0740.030
instrumentalness0.127-0.0830.0460.012-0.1340.0770.0350.0870.1190.076-0.0161.000-0.1790.1170.057-0.1530.0620.0820.0760.005-0.0460.0390.0170.0470.041-0.1020.1620.045
len0.071-0.225-0.272-0.2580.256-0.4620.258-0.416-0.344-0.3610.122-0.1791.000-0.332-0.3550.236-0.260-0.378-0.333-0.0540.272-0.475-0.363-0.2950.1800.165-0.121-0.268
light/visual perceptions0.0080.1030.109-0.027-0.1350.125-0.1230.1520.1870.102-0.0230.117-0.3321.0000.112-0.1300.1050.2020.106-0.029-0.1090.1750.0760.0760.049-0.1140.1050.116
like/girls-0.0250.0800.0810.073-0.0490.271-0.0890.2050.1280.166-0.0290.057-0.3550.1121.000-0.0750.1160.1620.1310.120-0.0810.2040.0690.1810.025-0.0260.0220.068
loudness0.149-0.540-0.502-0.0540.022-0.1040.768-0.139-0.121-0.0800.187-0.1530.236-0.130-0.0751.000-0.075-0.192-0.0680.0430.502-0.188-0.0950.0060.0820.0980.068-0.087
movement/places-0.0070.0710.077-0.048-0.0500.126-0.0540.2060.1570.067-0.0950.062-0.2600.1050.116-0.0751.0000.1500.0780.133-0.0770.113-0.0140.1200.054-0.0020.0820.035
music-0.0330.1880.1810.042-0.1130.180-0.1960.2110.2180.137-0.0900.082-0.3780.2020.162-0.1920.1501.0000.1100.018-0.1810.1860.0790.1130.363-0.0550.0080.054
night/time-0.0180.0500.0860.028-0.0660.202-0.0610.1380.0900.160-0.0450.076-0.3330.1060.131-0.0680.0780.1101.0000.003-0.0860.1510.0580.1000.363-0.0290.0020.012
obscene0.090-0.039-0.076-0.0110.1450.1300.0510.1410.0290.036-0.0290.005-0.054-0.0290.1200.0430.1330.0180.0031.0000.076-0.033-0.1930.1990.3720.084-0.023-0.094
release_date0.343-0.403-1.000-0.0540.170-0.1510.370-0.144-0.127-0.0980.085-0.0460.272-0.109-0.0810.502-0.077-0.181-0.0860.0761.000-0.218-0.100-0.0140.115-0.1050.075-0.078
romantic-0.0910.2190.2180.112-0.1420.294-0.2190.2150.1590.192-0.0880.039-0.4750.1750.204-0.1880.1130.1860.151-0.033-0.2181.0000.1600.1290.360-0.0820.0150.092
sadness-0.0890.1140.1000.066-0.1510.133-0.1430.0860.0900.071-0.0250.017-0.3630.0760.069-0.095-0.0140.0790.058-0.193-0.1000.1601.0000.0140.369-0.135-0.0750.019
shake the audience0.030-0.0010.0140.059-0.0320.2380.0000.1910.1080.148-0.0010.047-0.2950.0760.1810.0060.1200.1130.1000.199-0.0140.1290.0141.0000.030-0.0140.0460.034
topic0.1010.1070.1150.0300.1120.0450.1140.0150.0460.3600.1500.0410.1800.0490.0250.0820.0540.3630.3630.3720.1150.3600.3690.0301.000-0.1300.2880.504
valence-0.027-0.1110.105-0.0430.492-0.0140.265-0.033-0.060-0.035-0.006-0.1020.165-0.114-0.0260.098-0.002-0.055-0.0290.084-0.105-0.082-0.135-0.014-0.1301.000-0.099-0.110
violence0.144-0.145-0.0750.005-0.131-0.0130.1240.1080.2110.0430.0740.162-0.1210.1050.0220.0680.0820.0080.002-0.0230.0750.015-0.0750.0460.288-0.0991.000-0.037
world/life0.0120.0630.078-0.005-0.1030.050-0.1090.0920.1530.1040.0300.045-0.2680.1160.068-0.0870.0350.0540.012-0.094-0.0780.0920.0190.0340.504-0.110-0.0371.000

Missing values

2024-01-18T20:04:29.382051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-18T20:04:29.875328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Unnamed: 0artist_nametrack_namerelease_dategenrelyricslendatingviolenceworld/lifenight/timeshake the audiencefamily/gospelromanticcommunicationobscenemusicmovement/placeslight/visual perceptionsfamily/spirituallike/girlssadnessfeelingsdanceabilityloudnessacousticnessinstrumentalnessvalenceenergytopicage
00mukeshmohabbat bhi jhoothi1950pophold time feel break feel untrue convince speak voice tear try hold hurt try forgive okay play break string feel heart want feel tell real truth hurt lie worse anymore little turn dust play house ruin run leave save like chase train late late tear try hold hurt try forgive okay play break string feel heart want feel tell real truth hurt lie worse anymore little run leave save like chase train know late late play break string feel heart want feel tell real truth hurt lie worse anymore little know little hold time feel950.0005980.0637460.0005980.0005980.0005980.0488570.0171040.2637510.0005980.0392880.0005980.0005980.0005980.0005980.3802990.1171750.3577390.4541190.9979920.9018220.3394480.137110sadness1.0
14frankie lainei believe1950popbelieve drop rain fall grow believe darkest night candle glow believe go astray come believe believe believe smallest prayer hear believe great hear word time hear bear baby touch leaf believe believe believe lord heaven guide sin hide believe calvary die pierce believe death rise meet heaven loud amen know believe510.0355370.0967770.4434350.0012840.0012840.0270070.0012840.0012840.0012840.1180340.0012840.2126810.0511240.0012840.0012840.0012840.3317450.6475400.9548190.0000020.3250210.263240world/life1.0
26johnnie raycry1950popsweetheart send letter goodbye secret feel better wake dream think real false emotions feel heartaches hang long blue get bluer song remember cloudy hair240.0027700.0027700.0027700.0027700.0027700.0027700.1585640.2506680.0027700.3237940.0027700.0027700.0027700.0027700.0027700.2254220.4562980.5852880.8403610.0000000.3518140.139112music1.0
310pérez pradopatricia1950popkiss lips want stroll charm mambo chacha meringue heaven arm japan brag geisha care long uncle eye starry sort gleam like million dollar dream come true everybody wish steal heart away guess try eye starry sort gleam like million dollar dream come true kiss lips want stroll charm japan brag geisha care long uncle540.0482490.0015480.0015480.0015480.0215000.0015480.4115360.0015480.0015480.0015480.1292500.0015480.0015480.0811320.2258890.0015480.6869920.7444040.0839350.1993930.7753500.743736romantic1.0
412giorgos papadopoulosapopse eida oneiro1950poptill darling till matter know till dream live apart know hearts till world free oceans cross mountains climb know gain loss pray loss time till dream till memory till hold till oceans cross mountains climb know gain loss pray loss time till dream till memory till hold till480.0013500.0013500.4177720.0013500.0013500.0013500.4634300.0013500.0013500.0013500.0013500.0013500.0297550.0013500.0688000.0013500.2916710.6464890.9759040.0002460.5970730.394375romantic1.0
514perry comoround and round (with mitchell ayres and his orchestra & the ray charles singers)1950popconvoy light dead ahead merchantmen trump diesels hammer oily kill grind knuckle white eye alight slam hatch deadly night cunning chicken lair hound hell devil care run silent run deep final prayer warriors secret sleep merchantman nightmare silent death lie wait run silent run deep sink final sleep chill hearts fight open ocean wonder lethal silver fish boat shiver cast millions play killer victim fool obey order rehearse lifeboat shatter hull tear black smell burn jones eye watch crosswire tube ready medal chest weeks dead like rest run silent run deep final prayer warriors secret sleep merchantman nightmare980.0010530.4206850.0010530.0740780.0010530.0010530.0010530.0010530.0010530.0010530.0010530.1721730.0010530.0010530.1282920.0010530.6891580.6855880.8985940.0000000.7681370.549535violence1.0
615freestyleopm medley: when i met you1950poppiece mindin world knowin life come bring give world know give reason feelin give mean life go exist begin touch hair look eye know know good sure endure light world care away ache give reason feelin give mean life go exist begin bakit ganyan bakit ganyan ibig lagi kang pagmasdan umula umaraw hindi pagsasawaan iyong katangian damdamin ibangiba kapag kapiling sinta bakit ganyan damdamin maintindihan kailangan pagibig dahil nagmamahal magmula nang kita makilala bakit ganyan kung minsan nauutal kaba kapag kausap ngunit lumalakas loob kung ikaw nakatawa bakit ganyan damdamin maintindihan kailangan pagibig dahil nagmamahal magmula nang kita makilala think inlove feel restless inside want want thoughts night mind think think think single single night single moment life want spend think think think tell care tell tell feel word explain happen fast exactly feel right know long madness cause think think think single single night single moment life want spend think think think tell care tell tell feel medley give reason feelin give mean life go exist begin give reason feelin give mean life go exist begin think inlove1790.0004830.0004830.3718170.0407120.0004830.0004830.0004830.3847030.0004830.0004830.0004830.0443550.0004830.0004830.0004830.1231500.3685690.6336950.4357420.0000000.2827700.486470world/life1.0
717johnny mathisit's not for me to say1950popcare moment hold fast press lips dream heaven speak share glow grow pass meet break speak share glow grow pass meet210.0025060.0025060.3360560.0025060.0025060.0025060.1768610.0025060.0025060.0025060.0025060.3868870.0025060.0025060.0626020.0025060.3794000.5294210.9257030.0000720.3734540.192167world/life1.0
820stélios kazantzídisklapse me mana klapse me1950poplonely night surround power read mind hour night kiss lips hold tight unending real begin lonely night surround know certain flirtin night kiss lips hold tight baby maybe baby maybe300.0835370.0020240.0020240.2482730.0020240.0020240.4018010.1677830.0020240.0020240.0020240.0020240.0020240.0020240.0020240.0316000.4963720.6661800.9749000.0000140.6218050.400382romantic1.0
923stélios kazantzídisfinito la mouzika1950poptear heart seat stay awhile tear heart game steal glimpse eye stare awhile steal glimpse eye game half awhile half game word mouth swallow speak awhile word mouth game offer lose say mean right look size right tear heart seat stay awhile tear heart game steal glimpse eye stare awhile steal glimpse eye game offer lose say mean right look size610.0011200.1025480.0011200.0539440.0011200.0011200.0011200.0915720.0011200.0011200.1057520.0011200.0011200.0011200.6305070.0011200.6620820.6431300.9839360.0001750.7444350.413395sadness1.0
Unnamed: 0artist_nametrack_namerelease_dategenrelyricslendatingviolenceworld/lifenight/timeshake the audiencefamily/gospelromanticcommunicationobscenemusicmovement/placeslight/visual perceptionsfamily/spirituallike/girlssadnessfeelingsdanceabilityloudnessacousticnessinstrumentalnessvalenceenergytopicage
2836282439rakimwhen i b on tha mic2019hip hopinternationally know hardcore real niggas internationally know hardcore real niggas internationally know hardcore real niggas hail honorable real niggas heavyweight hitters dough getters ways figure niggas come spot feel sisters like hear real spitters kid ziggaziggas ugly club lovely thugs sip hennessey bubbly comrades flame dangerous block claim spot goal topranked soldier fortyfive holders high rollers lyric commercial580.0017540.0017540.0017540.0017540.0017540.0017540.0332250.0017540.7222330.0017540.0017540.0550280.0017540.0017540.0017540.0957450.8386220.7262260.0455810.0000000.8392420.484468obscene0.014286
2836382440ja rulekill 'em all2019hip hopspit shit damn nigga ridiculous nigga lose inconspicuous incognito niggas ready flow nigga know spit deadly fear dead street hole ghettos gradually disaster tear laughter gonna style touchin nigga wipe obvious lie dead wise guy bitch niggaz feel fuckin break long wrong dead go bomb clarify vain think motherfuckers playin baby lord deny long alive want piece respect till date demise baby kill thinkin game playin operatin like plan baby kill whatcha wanna lyric commercial750.0682140.2424610.0010740.0738360.0010740.0226110.0010740.0010740.4117520.0010740.0010740.0010740.0010740.0010740.0010740.0437490.7519770.7324560.0036840.0000130.6898190.833829obscene0.014286
2836482442nipsey husslehussle in the house2019hip hoplook comin straight slauson crazy motherfucker nipsey turf cause grow sixties caution niggas diss hard bitch wanna flip gun niggas turn rival rosaries extend clip fuck pose straight block sell dope groceries money advance royalties break nigga follow fuck bitch money hollow tip logically homicide boost economy tax corner work policy hustle white chalk corner yellow tape choppers cause turnin run streets small introduction nipsey hussle music plus money bitch grind shine come daytime light fast chain swang gettin dollars like doctor gangbang yeah hussle house lyric commercial880.0010960.0010960.0010960.0010960.0338290.0010960.0010960.0010960.7339590.0010960.0010960.1226270.0010960.0010960.0010960.0010960.6350050.8517550.0141560.0000000.7351610.913911obscene0.014286
2836582445nappy rootscountry boyz2019hip hophook country boys country walk country talk bring round know jumpin country boys country walk country talk bring round know jumpin stille uhhh nigga game game hanes shirt roll chain chain doorag heavy blue south drive fast fast niggaz roll billies dutches dutches want brand cartel lemme key cutlass cutlass represent macktown macktown stay smokin smackdown little half pound half pound know stille drillin black folks livin court week givin fuck grow standin grow women grow women stay high play till home wittem whattchu thinkin whattchu drinkin thinkin trickin trippin thinkin come hook skinny deville nigga hook like waitress ihop nothin grit steak waitin dollar pancake frontback lyric commercial1090.0008100.0008100.0008100.0008100.0008100.0008100.0008100.0008100.3942720.0008100.5153790.0008100.0008100.0008100.0008100.0008100.7660570.7733250.0373480.0000000.6826050.855851obscene0.014286
2836682446the rootsthe seed (2.0)2019hip hopknock months finna know want neosoul hiphop want rocknroll want platinum gold want somethin fold obstacle drop cold cause monkey stop little streets heat begin tell girl look calm hold hand enable peep plan cause quick learn money burn allow latest game room spread wing world finna know days bitch whine wanna fertilize lover watch grow standin fertilize lover lyric commercial610.0012240.0012240.1051720.0012240.0012240.0266740.1008560.0012240.3253590.0012240.0012240.1658830.0012240.0473100.0012240.0012240.7541430.8557030.0391560.0000000.9711460.957957obscene0.014286
2836782447mack 1010 million ways2019hip hopcause fuck leave scar tick tock clock come knock door wish like genie strait pay stack loot slangin dope mammas house grade try tiots looter hang drive shooters stickem honda scooters schollar throw hand holla wolla go high slangin joint dollar finger wave spot rag niggas dump blue rag niggas serve bellin hood honeycomb jersey painter pant cuff fixit biscuits till hair long brade bitch twist niggarunnin wild punch pedal meadow ghetto cause fuck leave scar lyric commercial780.0013500.0013500.0013500.0013500.0013500.0013500.0013500.0013500.3916510.0013500.4350890.0013500.0013500.0013500.0656640.0013500.8895270.7597110.0625490.0000000.7516490.695686obscene0.014286
2836882448m.o.p.ante up (robbin hoodz theory)2019hip hopminks things chain ring braclets yap fame come fool want stiflin fool fool want life jewel rule thing clap respect brooklyn bind bind brownsville home brave work street like slave rugged dress code stress mode think know blow nigga hold blow nigga hold blow nigga hold street cousin know drill ninety thou short ante fool ante kidnap fool perfect timin shinin damn diamonds ante fool lyric commercial670.0012840.0012840.0353380.0012840.0012840.0012840.0663240.2038890.3189100.0581520.1349550.0012840.0012840.0408110.0012840.0012840.6620820.7895800.0046070.0000020.9227120.797791obscene0.014286
2836982449ninewhutcha want?2019hip hopget ban get ban stick crack relax plan attack test pose finesse buddha bless nest chill peep steady bounce jeeps york streets hittin concrete untestable flow fiftyfix celo ounce bottle follow role model hollow tip clip money grip glock spit react bullshit room breathe deez save confessions jeesuz plus need hear sorrow come tomorrow long breathe need like achieve gettin cheese representin lovely boogie bronx project flavor daze behavior know want whutcha want beat rhyme lyric commercial770.0015040.1543020.1689880.0015040.0397550.0015040.0354010.0015040.3566850.0015040.0686840.0015040.0015040.0015040.0015040.0015040.6631650.7269700.1044170.0000010.8382110.767761obscene0.014286
2837082450will smithswitch2019hip hopcheck check yeah yeah hear thing call switch go switch turn turn switch turn vibe vibe second club girl arrive neckid hear veteran glide record download record sexy bout girl floor friends mean real clean gotta touch nuttin like like chick chick sumtin sucker track gimme drink chick tell stop dance clap flip round bring break switch turn turn switch turn switch turn turn switch lyric commercial670.0011960.0011960.0011960.0011960.0483590.0011960.0011960.0011960.4924340.1036140.0011960.2026590.0011960.0708670.0011960.0011960.8830280.7868880.0070270.0005030.5084500.885882obscene0.014286
2837182451jeezyr.i.p.2019hip hopremix killer alive remix thriller trap bitch spell damn cause hood nigga everybody list club fuck average watch fuck wrist average hoe fuck bitch window motherfuckin wall money jeans motherfuckin mall choppas bulletproof hummer competition summer wanna kill judge tryna lock homie feel thug thirsty fuck fuckin cause teach fuck stack money go hollywood funny know want pipe like crack bummy stop playin tryna like crash dummy start shit shit bitch bitch see snow bitch cancel like nino bitch kill club lyric commercial830.0010120.0752020.0010120.0010120.0010120.0010120.0010120.0010120.7544440.0462840.0010120.0010120.0010120.0010120.0010120.0339950.8288750.6747940.0158620.0000000.4754740.492477obscene0.014286